[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Automatic face recognition is now widely used in applications ranging from deduplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose an efficient and rather robust face spoof detection algorithm based on image distortion analysis (IDA). Four different features (specular reflection, blurriness, chromatic moment, and color diversity) are extracted to form the IDA feature vector. An ensemble classifier, consisting of multiple SVM classifiers trained for different face spoof attacks (e.g., printed photo and replayed video), is used to distinguish between genuine (live) and spoof faces. The proposed approach is extended to multiframe face spoof detection in videos using a voting-based scheme. We also collect a face spoof database, MSU mobile face spoofing database (MSU MFSD), using two mobile devices (Google Nexus 5 and MacBook Air) with three types of spoof attacks (printed photo, replayed video with iPhone 5S, and replayed video with iPad Air). Experimental results on two public-domain face spoof databases (Idiap REPLAY-ATTACK and CASIA FASD), and the MSU MFSD database show that the proposed approach outperforms the state-of-the-art methods in spoof detection. Our results also highlight the difficulty in separating genuine and spoof faces, especially in cross-database and cross-device scenarios.

/pdf/face-spoof-detection-with-image-distortion-analysis-46vrrako4a.pdf

Face Spoof Detection With Image Distortion Analysis

Everyday, people flexibly perform different categorizations of common faces, objects and scenes. Intuition and scattered evidence suggest that these categorizations require the use of different visual information from the input. However, there is no unifying method, based on the categorization performance of subjects, that can isolate the information used. To this end, we developed Bubbles, a general technique that can assign the credit of human categorization performance to specific visual information. To illustrate the technique, we applied Bubbles on three categorization tasks (gender, expressive or not and identity) on the same set of faces, with human and ideal observers to compare the features they used.

Bubbles: A Technique to Reveal the Use of Information in Recognition Tasks EOA Report

Face anti-spoofing is crucial to prevent face recognition systems from a security breach. Previous deep learning approaches formulate face anti-spoofing as a binary classification problem. Many of them struggle to grasp adequate spoofing cues and generalize poorly. In this paper, we argue the importance of auxiliary supervision to guide the learning toward discriminative and generalizable cues. A CNN-RNN model is learned to estimate the face depth with pixel-wise supervision, and to estimate rPPG signals with sequence-wise supervision. The estimated depth and rPPG are fused to distinguish live vs. spoof faces. Further, we introduce a new face anti-spoofing database that covers a large range of illumination, subject, and pose variations. Experiments show that our model achieves the state-of-the-art results on both intra- and cross-database testing.

/pdf/learning-deep-models-for-face-anti-spoofing-binary-or-1ekom3p7fk.pdf

Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision

Research on non-intrusive software-based face spoofing detection schemes has been mainly focused on the analysis of the luminance information of the face images, hence discarding the chroma component, which can be very useful for discriminating fake faces from genuine ones. This paper introduces a novel and appealing approach for detecting face spoofing using a colour texture analysis. We exploit the joint colour-texture information from the luminance and the chrominance channels by extracting complementary low-level feature descriptions from different colour spaces. More specifically, the feature histograms are computed over each image band separately. Extensive experiments on the three most challenging benchmark data sets, namely, the CASIA face anti-spoofing database, the replay-attack database, and the MSU mobile face spoof database, showed excellent results compared with the state of the art. More importantly, unlike most of the methods proposed in the literature, our proposed approach is able to achieve stable performance across all the three benchmark data sets. The promising results of our cross-database evaluation suggest that the facial colour texture representation is more stable in unknown conditions compared with its gray-scale counterparts.

Face Spoofing Detection Using Colour Texture Analysis

Matching near-infrared to visible images is one of the heterogeneous face recognition challenges in which spectral variations cause changes in the appearance of face images. In this paper, we propose to utilize a keypoint selection approach in the recognition pipeline. The proposed keypoint selection approach is a fast approximation of feature selection approach, yielding two orders of magnitude improvement in computational time while maintaining the recognition performance with respect to feature selection. The keypoint selection approach also enables to visualize the keypoints that are important for recognition. The proposed matching framework yields state-of-the-art approaches results on CASIA NIR-VIS-2.0 dataset.

http://www.iab-rubric.org/papers/2015_BTAS_Feature&Keypoint.pdf

Feature and keypoint selection for visible to near-infrared face matching

In surveillance applications, a video can be an advantageous alternative to traditional still image face recognition. In order to extract discriminative features from a video, frame based processing is preferred; however, not all frames are suitable for face recognition. While some frames are distorted due to noise, blur, and occlusion, others might be affected by the presence of covariates such as pose, illumination, and expression. Frames affected by imaging artifacts such as noise and blur may not contain reliable facial information and may affect the recognition performance. In an ideal scenario, such frames should not be considered for feature extraction and matching. Furthermore, video contains a large amount of frames and adjacent frames contain largely redundant information and processing all the frames from a video increases the computational complexity. Instead of utilizing all the frames from a video, frame selection can be performed to determine a subset of frames which is best suited for face recognition. Several frame selection algorithms have been proposed in the literature to address these concerns. In this chapter, we discuss the role and importance of frame selection in video face recognition, provide an overview of existing techniques, present an entropy based frame selection algorithm with the results and analysis on Point-and-Shoot-Challenge which is a recent benchmark database, and also propose a new paradigm for frame selection algorithms as a path forward.

On Frame Selection for Video Face Recognition

Over the years, automatic gender recognition has been used in many applications. However, limited research has been done on analyzing gender recognition across ethnicity scenario. This research aims at studying the performance of discriminant functions including Principal Component Analysis, Linear Discriminant Analysis and Subclass Discriminant Analysis with the availability of limited training database and unseen ethnicity variations. The experiments are performed on a heterogeneous database of 8112 images that includes variations in illumination, expression, minor pose and ethnicity. Contrary to existing literature, the results show that PCA provides comparable but slightly better performance compared to PCA+LDA, PCA+SDA and PCA+SVM. The results also suggest that linear discriminant functions provide good generalization capability even with limited number of training samples, principal components and with cross-ethnicity variations.

Is gender classification across ethnicity feasible using discriminant functions

Accurately interpreting student responses is a critical requirement of dialog-based intelligent tutoring systems. The accuracy of supervised learning methods, used for interpreting or analyzing student responses, is strongly dependent on the availability of annotated training data. Collecting and grading student responses is tedious, time-consuming, and expensive. This work proposes an iterative data collection and grading approach. We show that data collection efforts can be significantly reduced by predicting question difficulty and by collecting answers from a focused set of students. Further, grading efforts can be reduced by filtering student answers that may not be helpful in training Student Response Analyzer (SRA). To ensure the quality of grades, we analyze the grader characteristics, and show improvement when a biased grader is removed. An experimental evaluation on a large scale dataset shows a reduction of up to 28% in the data collection cost, and up to 10% in grading cost while improving the response analysis macro-average F1.

Balancing Human Efforts and Performance of Student Response Analyzer in Dialog-Based Tutors

Subclass discriminant analysis is found to be applicable under various scenarios. However, it is computationally expensive to update the between-class and within-class scatter matrices in batch mode. This research presents an incremental subclass discriminant analysis algorithm to update SDA in incremental manner with increasing number of samples per class. The effectiveness of the proposed algorithm is demonstrated using face recognition in terms of identification accuracy and training time. Experiments are performed on the AR face database and compared with other subspace based incremental and batch learning algorithms. The results illustrate that, compared to SDA, incremental SDA yields significant reduction in time along with comparable accuracy.

Tejas I. Dhamecha

Papers

Feature and keypoint selection for visible to near-infrared face matching

On Frame Selection for Video Face Recognition

Is gender classification across ethnicity feasible using discriminant functions

Balancing Human Efforts and Performance of Student Response Analyzer in Dialog-Based Tutors

Incremental subclass discriminant analysis: A case study in face recognition