This paper concerns the estimation of facial attributes—namely, age and gender—from images of faces acquired in challenging, in the wild conditions This problem has received far less attention than the related problem of face recognition, and in particular, has not enjoyed the same dramatic improvement in capabilities demonstrated by contemporary face recognition systems Here, we address this problem by making the following contributions First, in answer to one of the key problems of age estimation research—absence of data—we offer a unique data set of face images, labeled for age and gender, acquired by smart-phones and other mobile devices, and uploaded without manual filtering to online image repositories We show the images in our collection to be more challenging than those offered by other face-photo benchmarks Second, we describe the dropout-support vector machine approach used by our system for face attribute estimation, in order to avoid over-fitting This method, inspired by the dropout learning techniques now popular with deep belief networks, is applied here for training support vector machines, to the best of our knowledge, for the first time Finally, we present a robust face alignment technique, which explicitly considers the uncertainties of facial feature detectors We report extensive tests analyzing both the difficulty levels of contemporary benchmarks as well as the capabilities of our own system These show our method to outperform state-of-the-art by a wide margin

/pdf/age-and-gender-estimation-of-unfiltered-faces-2ujy89s1qr.pdf

Age and Gender Estimation of Unfiltered Faces

Facial expression recognition with Convolutional Neural Networks

This paper presents a new approach to improve the performance of finger-vein identification systems presented in the literature. The proposed system simultaneously acquires the finger-vein and low-resolution fingerprint images and combines these two evidences using a novel score-level combination strategy. We examine the previously proposed finger-vein identification approaches and develop a new approach that illustrates it superiority over prior published efforts. The utility of low-resolution fingerprint images acquired from a webcam is examined to ascertain the matching performance from such images. We develop and investigate two new score-level combinations, i.e., holistic and nonlinear fusion, and comparatively evaluate them with more popular score-level fusion approaches to ascertain their effectiveness in the proposed system. The rigorous experimental results presented on the database of 6264 images from 156 subjects illustrate significant improvement in the performance, i.e., both from the authentication and recognition experiments.

Human Identification Using Finger Images

A survey of palmprint recognition

Palmprint is a unique and reliable biometric characteristic with high usability. With the increasing demand of highly accurate and robust palmprint authentication system, multispectral imaging has been employed to acquire more discriminative information and increase the antispoof capability of palmprint. This paper presents an online multispectral palmprint system that could meet the requirement of real-time application. A data acquisition device is designed to capture the palmprint images under Blue, Green, Red, and near-infrared (NIR) illuminations in less than 1 s. A large multispectral palmprint database is then established to investigate the recognition performance of each spectral band. Our experimental results show that the red channel achieves the best result, whereas the Blue and Green channels have comparable performance but are slightly inferior to the NIR channel. After analyzing the extracted features from different bands, we propose a score level fusion scheme to integrate the multispectral information. The palmprint verification experiments demonstrated the superiority of multispectral fusion to each single spectrum, which results in both higher verification accuracy and antispoofing capability.

/pdf/an-online-system-of-multispectral-palmprint-verification-222youf4nj.pdf

An Online System of Multispectral Palmprint Verification

Fingerprint friction ridge details are generally described in a hierarchical order at three different levels, namely, level 1 (pattern), level 2 (minutia points), and level 3 (pores and ridge contours). Although latent print examiners frequently take advantage of level 3 features to assist in identification, automated fingerprint identification systems (AFIS) currently rely only on level 1 and level 2 features. In fact, the Federal Bureau of Investigation's (FBI) standard of fingerprint resolution for AFIS is 500 pixels per inch (ppi), which is inadequate for capturing level 3 features, such as pores. With the advances in fingerprint sensing technology, many sensors are now equipped with dual resolution (500 ppi/1,000 ppi) scanning capability. However, increasing the scan resolution alone does not necessarily provide any performance improvement in fingerprint matching, unless an extended feature set is utilized. As a result, a systematic study to determine how much performance gain one can achieve by introducing level 3 features in AFIS is highly desired. We propose a hierarchical matching system that utilizes features at all the three levels extracted from 1,000 ppi fingerprint scans. Level 3 features, including pores and ridge contours, are automatically extracted using Gabor filters and wavelet transform and are locally matched using the iterative closest point (ICP) algorithm. Our experiments show that level 3 features carry significant discriminatory information. There is a relative reduction of 20 percent in the equal error rate (EER) of the matching system when level 3 features are employed in combination with level 1 and 2 features. This significant performance gain is consistently observed across various quality fingerprint images

/pdf/pores-and-ridges-high-resolution-fingerprint-matching-using-2h2belrnp4.pdf

Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features

Fingerprint friction ridge details are generally described in a hierarchical order at three levels, namely, Level 1 (pattern), Level 2 (minutiae points) and Level 3 (pores and ridge shape). Although high resolution sensors (~1000 dpi) have become commercially available and have made it possible to reliably extract Level 3 features, most automated fingerprint identification systems (AFIS) employ only Level 1 and Level 2 features. As a result, increasing the scan resolution does not provide any matching performance improvement (1998). We develop a matcher that utilizes Level 3 features, including pores and ridge contours, for 1000 dpi fingerprint matching. Level 3 features are automatically extracted using wavelet transform and Gabor filters and are locally matched using the ICP algorithm. Our experiments on a median-sized database show that Level 3 features carry significant discriminatory information. EER values are reduced (relatively ~20%) when Level 3 features are employed in combination with Level 1 and 2 features

/pdf/pores-and-ridges-fingerprint-matching-using-level-3-features-4xpir0tpv3.pdf

Pores and Ridges: Fingerprint Matching Using Level 3 Features

We describe the design and development of a prototype whole-hand imaging system. The sensor is based on multispectral technology that is able to provide hand shape, fingerprints and palmprint modalities of a user's hand by a single user interaction with the sensor. A clear advantage of our system over other unimodal sensors for these modalities include: (i) faster acquisition time, (ii) better quality images, and (iii) ability to provide spoof detection. Initial results on a medium-size database show good recognition performance based on individual modalities as well as after fusing multiple fingers and fusing finger and palm. The prototype is being refined in order to improve performance even further.

/pdf/a-multispectral-whole-hand-biometric-authentication-system-1qv2s1o839.pdf

A Multispectral Whole-Hand Biometric Authentication System

Fingerprint recognition and matching systems and methods are described that utilize features at all three fingerprint friction ridge detail levels, i.e., Level 1, Level 2 and Level 3, extracted from 1000 ppi fingerprint scans. Level 3 features, including but not limited to pore and ridge contour characteristics, were automatically extracted using various filters (e.g., Gabor filters, edge detector filters, and/or the like) and transforms (e.g., wavelet transforms) and were locally matched using various algorithms (e.g., the iterative closest point (ICP) algorithm). Because Level 3 features carry significant discriminatory and complementary information, there was a relative reduction of 20% in the equal error rate (EER) of the matching system when Level 3 features were employed in combination with Level 1 and Level 2 features, which were also automatically extracted. This significant performance gain was consistently observed across various quality fingerprint images.

Level 3 features for fingerprint matching

We present a prototype video tracking and person categorization system that uses face and person soft biometric features
to tag people while tracking them in multiple camera views. Our approach takes advantage of temporal aspect of video
by extracting and accumulating feasible soft biometric features for each person in every frame to build a dynamic soft
biometric feature list for each tracked person in surveillance videos. We developed algorithms for extracting face soft
biometric features to achieve gender and ethnicity classification and session soft biometric features to aid in camera
hand-off in surveillance videos with low resolution and uncontrolled illumination. To train and test our face soft
biometry algorithms, we collected over 1500 face images from both genders and three ethnicity groups with various
sizes, poses and illumination. These soft biometric feature extractors and classifiers are implemented on our existing
video content extraction platform to enhance video surveillance tasks. Our algorithms achieved promising results for
gender and ethnicity classification, and tracked person re-identification for camera hand-off on low to good quality
surveillance and broadcast videos. By utilizing the proposed system, a high level description of extracted person's soft
biometric data can be stored to use later for different purposes, such as to provide categorical information of people, to
create database partitions to accelerate searches in responding to user queries, and to track people between cameras.

https://www.intuvisiontech.com/tech/SPIE_7667-23.pdf

Meltem Demirkus

Papers

Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features

Pores and Ridges: Fingerprint Matching Using Level 3 Features

A Multispectral Whole-Hand Biometric Authentication System

Level 3 features for fingerprint matching

Automated person categorization for video surveillance using soft biometrics