Showing papers on "Face detection published in 2011"

Face recognition using Eigenfaces

Abstract: Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages - Feature extraction using Principle Component Analysis and recognition using the feed forward back propagation Neural Network. The goal is to implement the system (model) for a particular face and distinguish it from a large number of stored faces with some real-time variations as well. The Eigenface approach uses Principal Component Analysis (PCA) algorithm for the recognition of the images. It gives us efficient way to find the lower dimensional space.

Handbook of Face Recognition

Abstract: This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational face recognition systems. After a thorough introductory chapter, each of the following chapters focus on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Features: fully updated, revised and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated face detection and recognition systems; provides comprehensive coverage of face detection, tracking, alignment, feature extraction, and recognition technologies, and issues in evaluation, systems, security, and applications; contains numerous step-by-step algorithms; describes a broad range of applications; presents contributions from an international selection of experts; integrates numerous supporting graphs, tables, charts, and performance data.

Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization

Abstract: Face alignment is a crucial step in face recognition tasks. Especially, using landmark localization for geometric face normalization has shown to be very effective, clearly improving the recognition results. However, no adequate databases exist that provide a sufficient number of annotated facial landmarks. The databases are either limited to frontal views, provide only a small number of annotated images or have been acquired under controlled conditions. Hence, we introduce a novel database overcoming these limitations: Annotated Facial Landmarks in the Wild (AFLW). AFLW provides a large-scale collection of images gathered from Flickr, exhibiting a large variety in face appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions. In total 25,993 faces in 21,997 real-world images are annotated with up to 21 landmarks per image. Due to the comprehensive set of annotations AFLW is well suited to train and test algorithms for multi-view face detection, facial landmark localization and face pose estimation. Further, we offer a rich set of tools that ease the integration of other face databases and associated annotations into our joint framework.

Local Binary Patterns and Its Application to Facial Image Analysis: A Survey

Abstract: Local binary pattern (LBP) is a nonparametric descriptor, which efficiently summarizes the local structures of images. In recent years, it has aroused increasing interest in many areas of image processing and computer vision and has shown its effectiveness in a number of applications, in particular for facial image analysis, including tasks as diverse as face detection, face recognition, facial expression analysis, and demographic classification. This paper presents a comprehensive survey of LBP methodology, including several more recent variations. As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial image analysis, are also highlighted.

Face spoofing detection from single images using micro-texture analysis

Abstract: Current face biometric systems are vulnerable to spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterization of printing artifacts, and differences in light reflection, we propose to approach the problem of spoofing detection from texture analysis point of view. Indeed, face prints usually contain printing quality defects that can be well detected using texture features. Hence, we present a novel approach based on analyzing facial image textures for detecting whether there is a live person in front of the camera or a face print. The proposed approach analyzes the texture of the facial images using multi-scale local binary patterns (LBP). Compared to many previous works, our proposed approach is robust, computationally fast and does not require user-cooperation. In addition, the texture features that are used for spoofing detection can also be used for face recognition. This provides a unique feature space for coupling spoofing detection and face recognition. Extensive experimental analysis on a publicly available database showed excellent results compared to existing works.

Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition

Abstract: In video based face recognition, face images are typically captured over multiple frames in uncontrolled conditions, where head pose, illumination, shadowing, motion blur and focus change over the sequence. Additionally, inaccuracies in face localisation can also introduce scale and alignment variations. Using all face images, including images of poor quality, can actually degrade face recognition performance. While one solution it to use only the ‘best’ of images, current face selection techniques are incapable of simultaneously handling all of the abovementioned issues. We propose an efficient patch-based face image quality assessment algorithm which quantifies the similarity of a face image to a probabilistic face model, representing an ‘ideal’ face. Image characteristics that affect recognition are taken into account, including variations in geometric alignment (shift, rotation and scale), sharpness, head pose and cast shadows. Experiments on FERET and PIE datasets show that the proposed algorithm is able to identify images which are simultaneously the most frontal, aligned, sharp and well illuminated. Further experiments on a new video surveillance dataset (termed ChokePoint) show that the proposed method provides better face subsets than existing face selection techniques, leading to significant improvements in recognition accuracy.

An associate-predict model for face recognition

Abstract: Handling intra-personal variation is a major challenge in face recognition. It is difficult how to appropriately measure the similarity between human faces under significantly different settings (e.g., pose, illumination, and expression). In this paper, we propose a new model, called “Associate-Predict” (AP) model, to address this issue. The associate-predict model is built on an extra generic identity data set, in which each identity contains multiple images with large intra-personal variation. When considering two faces under significantly different settings (e.g., non-frontal and frontal), we first “associate” one input face with alike identities from the generic identity date set. Using the associated faces, we generatively “predict” the appearance of one input face under the setting of another input face, or discriminatively “predict” the likelihood whether two input faces are from the same person or not. We call the two proposed prediction methods as “appearance-prediction” and “likelihood-prediction”. By leveraging an extra data set (“memory”) and the “associate-predict” model, the intra-personal variation can be effectively handled. To improve the generalization ability of our model, we further add a switching mechanism — we directly compare the appearances of two faces if they have close intra-personal settings; otherwise, we use the associate-predict model for the recognition. Experiments on two public face benchmarks (Multi-PIE and LFW) demonstrated that our final model can substantially improve the performance of most existing face recognition methods

Face Processing: Advanced Modeling and Methods

Abstract: Preface PART I: THE BASICS Chapter 1: A Guided Tour for Face Processing Chapter 2: Eigenface and Beyond Chapter 3: Statistical Evaluation of Face Recognition Systems PART II: FACE MODELING Chapter 4: 3D Morphable Face Model: A Unified Approach for Analysis and Synthesis of Images Chapter 5: Expression-Invariant Three-Dimensional Face Recognition Chapter 6: 3D Face Modeling from Monocular Video Sequences Chapter 7: Face Modeling by Information Maximization Chapter 8: Face Recognition by Human Chapter 9: Predicting Face Recognition Success for Humans Chapter 10: Distributed Representation of Faces and Objects PART III: ADVANCED METHODS Chapter 11: On the Effect of Illumination and Face Recognition Chapter 12: Modeling Illumination Variation with Spherical Harmonics Chapter 13: Multi-Subregion Based Probabilistic Approach Toward Pose-Invariant Face Recognition Chapter 14:Morphable Models for Training a Component-Based Face Recognition System Chapter 15: Model-Based Face Modeling and Tracking with Application to Video Conferencing Chapter 16: 3D and MultiModal 3D & 2D Face Recognition Chapter 17: Beyond One Still Image: Face Recognition from multiple Still Images or Video Sequence Chapter 18: Subset Modeling of Face Localization Error, Occlusion and Expression Chapter 19: Real-Time Robust Face and Facial Features Detection with Information-Based Maximum Discrimination Chapter 20: Current Landscape of Thermal Infrared Face Recognition Chapter 21: Multimodal Biometrics: Augmenting Face with Other Cues

Unsupervised metric learning for face identification in TV video

Abstract: The goal of face identification is to decide whether two faces depict the same person or not. This paper addresses the identification problem for face-tracks that are automatically collected from uncontrolled TV video data. Face-track identification is an important component in systems that automatically label characters in TV series or movies based on subtitles and/or scripts: it enables effective transfer of the sparse text-based supervision to other faces. We show that, without manually labeling any examples, metric learning can be effectively used to address this problem. This is possible by using pairs of faces within a track as positive examples, while negative training examples can be generated from pairs of face tracks of different people that appear together in a video frame. In this manner we can learn a cast-specific metric, adapted to the people appearing in a particular video, without using any supervision. Identification performance can be further improved using semi-supervised learning where we also include labels for some of the face tracks. We show that our cast-specific metrics not only improve identification, but also recognition and clustering.

Face liveness detection under bad illumination conditions

Abstract: Spoofing face recognition systems with photos or videos of someone else is not difficult. Sometimes, all one needs is to display a picture on a laptop monitor or a printed photograph to the biometric system. In order to detect this kind of spoofs, in this paper we present a solution that works either with printed or LCD displayed photographs, even under bad illumination conditions without extra-devices or user involvement. Tests conducted on large databases show good improvements of classification accuracy as well as true positive and false positive rates compared to the state-of-the-art.

Face Verification Using the LARK Representation

Abstract: We present a novel face representation based on locally adaptive regression kernel (LARK) descriptors. Our LARK descriptor measures a self-similarity based on “signal-induced distance” between a center pixel and surrounding pixels in a local neighborhood. By applying principal component analysis (PCA) and a logistic function to LARK consecutively, we develop a new binary-like face representation which achieves state-of-the-art face verification performance on the challenging benchmark “Labeled Faces in the Wild” (LFW) dataset. In the case where training data are available, we employ one-shot similarity (OSS) based on linear discriminant analysis (LDA). The proposed approach achieves state-of-the-art performance on both the unsupervised setting and the image restrictive training setting (72.23% and 78.90% verification rates), respectively, as a single descriptor representation, with no preprocessing step. As opposed to combined 30 distances which achieve 85.13%, we achieve comparable performance (85.1%) with only 14 distances while significantly reducing computational complexity.

Visual Cryptography for Biometric Privacy

Abstract: Preserving the privacy of digital biometric data (e.g., face images) stored in a central database has become of paramount importance. This work explores the possibility of using visual cryptography for imparting privacy to biometric data such as fingerprint images, iris codes, and face images. In the case of faces, a private face image is dithered into two host face images (known as sheets) that are stored in two separate database servers such that the private image can be revealed only when both sheets are simultaneously available; at the same time, the individual sheet images do not reveal the identity of the private image. A series of experiments on the XM2VTS and IMM face databases confirm the following: 1) the possibility of hiding a private face image in two host face images; 2) the successful matching of face images reconstructed from the sheets; 3) the inability of sheets to reveal the identity of the private face image; 4) using different pairs of host images to encrypt different samples of the same private face; and 5) the difficulty of cross-database matching for determining identities. A similar process is used to de-identify fingerprint images and iris codes prior to storing them in a central database.

Stable face representations

Abstract: Photographs are often used to establish the identity of an individual or to verify that they are who they claim to be. Yet, recent research shows that it is surprisingly difficult to match a photo to a face. Neither humans nor machines can perform this task reliably. Although human perceivers are good at matching familiar faces, performance with unfamiliar faces is strikingly poor. The situation is no better for automatic face recognition systems. In practical settings, automatic systems have been consistently disappointing. In this review, we suggest that failure to distinguish between familiar and unfamiliar face processing has led to unrealistic expectations about face identification in applied settings. We also argue that a photograph is not necessarily a reliable indicator of facial appearance, and develop our proposal that summary statistics can provide more stable face representations. In particular, we show that image averaging stabilizes facial appearance by diluting aspects of the image that vary between snapshots of the same person. We review evidence that the resulting images can outperform photographs in both behavioural experiments and computer simulations, and outline promising directions for future research.

Real-time clothing recognition in surveillance videos

Abstract: Recognition of clothing categories from videos is appealing to emerging applications such as intelligent customer profile analysis and computer-aided fashion design. This paper presents a complete system to tag clothing categories in real-time, which addresses some practical complications in surveillance videos. Specifically, we take advantage of face detection and tracking to locate human figures and develop an efficient clothing segmentation method utilizing Voronoi images to select seeds for region growing. We compare clothing representations combining color histograms and 3 different texture descriptors. Evaluated on a video dataset with 937 persons and 25441 cloth instances, the system demonstrates promising results in recognizing 8 clothing categories.

Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook

Abstract: Biological visual systems are currently unrivaled by artificial systems in their ability to recognize faces and objects in highly variable and cluttered real-world environments. Biologically-inspired computer vision systems seek to capture key aspects of the computational architecture of the brain, and such approaches have proven successful across a range of standard object and face recognition tasks (e.g. [23, 8, 9, 18]). Here, we explore the effectiveness of these algorithms on a large-scale unconstrained real-world face recognition problem based on images taken from the Face-book social networking website. In particular, we use a family of biologically-inspired models derived from a high-throughput feature search paradigm [19, 15] to tackle a face identification task with up to one hundred individuals (a number that approaches the reasonable size of real-world social networks). We show that these models yield high levels of face-identification performance even when large numbers of individuals are considered; this performance increases steadily as more examples are used, and the models outperform a state-of-the-art commercial face recognition system. Finally, we discuss current limitations and future opportunities associated with datasets such as these, and we argue that careful creation of large sets is an important future direction.

Monocular camera-based face liveness detection by combining eyeblink and scene context

Abstract: This paper presents a face liveness detection system against spoofing with photographs, videos, and 3D models of a valid user in a face recognition system. Anti-spoofing clues inside and outside a face are both exploited in our system. The inside-face clues of spontaneous eyeblinks are employed for anti-spoofing of photographs and 3D models. The outside-face clues of scene context are used for anti-spoofing of video replays. The system does not need user collaborations, i.e. it runs in a non-intrusive manner. In our system, the eyeblink detection is formulated as an inference problem of an undirected conditional graphical framework which models contextual dependencies in blink image sequences. The scene context clue is found by comparing the difference of regions of interest between the reference scene image and the input one, which is based on the similarity computed by local binary pattern descriptors on a series of fiducial points extracted in scale space. Extensive experiments are carried out to show the effectiveness of our system.

Face recognition: Some challenges in forensics

Abstract: Face recognition has become a valuable and routine forensic tool used by criminal investigators. Compared to automated face recognition, forensic face recognition is more demanding because it must be able to handle facial images captured under non-ideal conditions and it has high liability for following legal procedures. This paper discusses recent developments in automated face recognition that impact the forensic face recognition community. Improvements in forensic face recognition through research in facial aging, facial marks, forensic sketch recognition, face recognition in video, near-infrared face recognition, and use of soft biometrics will be discussed. Finally, current limitations and future research directions for face recognition in forensics are suggested.

Face spoofing detection through partial least squares and low-level descriptors

Abstract: Personal identity verification based on biometrics has received increasing attention since it allows reliable authentication through intrinsic characteristics, such as face, voice, iris, fingerprint, and gait. Particularly, face recognition techniques have been used in a number of applications, such as security surveillance, access control, crime solving, law enforcement, among others. To strengthen the results of verification, biometric systems must be robust against spoofing attempts with photographs or videos, which are two common ways of bypassing a face recognition system. In this paper, we describe an anti-spoofing solution based on a set of low-level feature descriptors capable of distinguishing between ‘live’ and ‘spoof’ images and videos. The proposed method explores both spatial and temporal information to learn distinctive characteristics between the two classes. Experiments conducted to validate our solution with datasets containing images and videos show results comparable to state-of-the-art approaches.

Comparing machines and humans on a visual categorization test

Abstract: Automated scene interpretation has benefited from advances in machine learning, and restricted tasks, such as face detection, have been solved with sufficient accuracy for restricted settings. However, the performance of machines in providing rich semantic descriptions of natural scenes from digital images remains highly limited and hugely inferior to that of humans. Here we quantify this “semantic gap” in a particular setting: We compare the efficiency of human and machine learning in assigning an image to one of two categories determined by the spatial arrangement of constituent parts. The images are not real, but the category-defining rules reflect the compositional structure of real images and the type of “reasoning” that appears to be necessary for semantic parsing. Experiments demonstrate that human subjects grasp the separating principles from a handful of examples, whereas the error rates of computer programs fluctuate wildly and remain far behind that of humans even after exposure to thousands of examples. These observations lend support to current trends in computer vision such as integrating machine learning with parts-based modeling.

Face detection using SURF cascade

Abstract: We present a novel boosting cascade based face detection framework using SURF features. The framework is derived from the well-known Viola-Jones (VJ) framework but distinguished by two key contributions. First, the proposed framework deals with only several hundreds of multidimensional local SURF patches instead of hundreds of thousands of single dimensional haar features in the VJ framework. Second, it takes AUC as a single criterion for the convergence test of each cascade stage rather than the two conflicting criteria (false-positive-rate and detection-rate) in the VJ framework. These modifications yield much faster training convergence and much fewer stages in the final cascade. We made experiments on training face detector from large scale database. Results shows that the proposed method is able to train face detectors within one hour through scanning billions of negative samples on current personal computers. Furthermore, the built detector is comparable to the state-of-the-art algorithm not only on the accuracy but also on the processing speed.

Initializing Camera Subsystem for Face Detection Based on Sensor Inputs

Abstract: In one embodiment, while a mobile electronic device is in a first operation state, it receives sensor data from one or more sensors of the mobile electronic device. The mobile electronic device in a locked state analyzes the sensor data to estimate whether an unlock operation is imminent, and in response to a positive determination, initializes the camera subsystem so that the camera is ready to capture a face as soon as the user directs the camera lens to his or her face. In particular embodiments, the captured image is utilized by a facial recognition algorithm to determine whether the user is authorized to use the mobile device. In particular embodiments, the captured facial recognition image may be leveraged for use on a social network.

A comparison of face and facial feature detectors based on the Viola–Jones general object detection framework

Abstract: The human face provides useful information during interaction; therefore, any system integrating Vision-Based Human Computer Interaction requires fast and reliable face and facial feature detection. Different approaches have focused on this ability but only open source implementations have been extensively used by researchers. A good example is the Viola–Jones object detection framework that particularly in the context of facial processing has been frequently used. The OpenCV community shares a collection of public domain classifiers for the face detection scenario. However, these classifiers have been trained in different conditions and with different data but rarely tested on the same datasets. In this paper, we try to fill that gap by analyzing the individual performance of all those public classifiers presenting their pros and cons with the aim of defining a baseline for other approaches. Solid comparisons will also help researchers to choose a specific classifier for their particular scenario. The experimental setup also describes some heuristics to increase the facial feature detection rate while reducing the face false detection rate.

UMB-DB: A database of partially occluded 3D faces

Abstract: In this paper we present the UMB-DB 3D face database. The database has been built to test algorithms and systems for 3D face analysis in uncontrolled and challenging scenarios, in particular in those cases where faces are occluded. The database is composed of 1473 pairs of depth and color images of 143 subjects. Each subject has been acquired with different facial expressions, and with the face partially occluded by various objects such as eyeglasses, hats, scarves and hands. The total number of occluded acquisitions is 578. The database, that is freely available for research purposes, could be used for various investigations, some of which are suggested in the paper. For the sake of comparison, we report the results of some of the 3D face detection and recognition algorithms in the state of the art.

Image recomposition from face detection and facial features

Abstract: A computing system for automatically identifying individual regions in a digital image that include a human face, forming combined padded regions and automatically evaluating each of the regions including calculating a fitness score for each of the combined padded regions and ignoring at least one of the combined padded regions whose fitness score is below a known threshold

Fusion of multiple clues for photo-attack detection in face recognition systems

Abstract: We faced the problem of detecting 2-D face spoofing attacks performed by placing a printed photo of a real user in front of the camera. For this type of attack it is not possible to relay just on the face movements as a clue of vitality because the attacker can easily simulate such a case, and also because real users often show a “low vitality” during the authentication session. In this paper, we perform both video and static analysis in order to employ complementary information about motion, texture and liveness and consequently to obtain a more robust classification.

Low-level cues and ultra-fast face detection.

Abstract: Recent experimental work has demonstrated the existence of extremely rapid saccades toward faces in natural scenes that can be initiated only 100 ms after image onset (Crouzet et al., 2010). These ultra-rapid saccades constitute a major challenge to current models of processing in the visual system because they do not seem to leave enough time for even a single feed-forward pass through the ventral stream. Here we explore the possibility that the information required to trigger these very fast saccades could be extracted very early on in visual processing using relatively low-level amplitude spectrum (AS) information in the Fourier domain. Experiment 1 showed that AS normalization can significantly alter face-detection performance. However, a decrease of performance following AS normalization does not alone prove that AS-based information is used (Gaspar and Rousselet, 2009). In Experiment 2, following the Gaspar and Rousselet paper, we used a swapping procedure to clarify the role of AS information in fast object detection. Our experiment is composed of three conditions: (i) original images, (ii) category swapped, in which the face image has the AS of a vehicle, and the vehicle has the AS of a face, and (iii) identity swapped, where the face has the AS of another face image, and the vehicle has the AS of another vehicle image. The results showed very similar levels of performance in the original and identity swapped conditions, and a clear drop in the category swapped condition. This result demonstrates that, in the early temporal window offered by the saccadic choice task, the visual saccadic system does indeed rely on low-level AS information in order to rapidly detect faces. This sort of crude diagnostic information could potentially be derived very early on in the visual system, possibly as early as V1 and V2.

An electronic travel aid for navigation of visually impaired persons

Abstract: This paper presents an electronic travel aid for blind people to navigate safely and quickly, an obstacle detection system using ultrasonic sensors and USB camera based visual navigation has been considered. The proposed system detects the obstacles up to 300 cm via sonar and sends feedback (beep sound) to inform the person about its location. In addition to this, an USB webcam is connected with eBox 2300™ Embedded System for capturing the field of view of the user, which is used for finding the properties of the obstacle in particular, in the context of this work, locating a human being. Identification of human presence is based on face detection and cloth texture analysis. The major constraints for these algorithms to run on Embedded System are small image frame (160×120) having reduced faces, limited memory and very less processing time available to achieve real time image processing requirements. The algorithms are implemented in C++ using Visual Studio 5.0 IDE, which runs on Windows CE™ environment.

Exploring photobios

Abstract: We present an approach for generating face animations from large image collections of the same person. Such collections, which we call photobios, sample the appearance of a person over changes in pose, facial expression, hairstyle, age, and other variations. By optimizing the order in which images are displayed and cross-dissolving between them, we control the motion through face space and create compelling animations (e.g., render a smooth transition from frowning to smiling). Used in this context, the cross dissolve produces a very strong motion effect; a key contribution of the paper is to explain this effect and analyze its operating range. The approach operates by creating a graph with faces as nodes, and similarities as edges, and solving for walks and shortest paths on this graph. The processing pipeline involves face detection, locating fiducials (eyes/nose/mouth), solving for pose, warping to frontal views, and image comparison based on Local Binary Patterns. We demonstrate results on a variety of datasets including time-lapse photography, personal photo collections, and images of celebrities downloaded from the Internet. Our approach is the basis for the Face Movies feature in Google's Picasa.