Zhiwei Zhang

A face antispoofing database with diverse attacks

Face antispoofing has now attracted intensive attention, aiming to assure the reliability of face biometrics. We notice that currently most of face antispoofing databases focus on data with little variations, which may limit the generalization performance of trained models since potential attacks in real world are probably more complex. In this paper we release a face antispoofing database which covers a diverse range of potential attack variations. Specifically, the database contains 50 genuine subjects, and fake faces are made from the high quality records of the genuine faces. Three imaging qualities are considered, namely the low quality, normal quality and high quality. Three fake face attacks are implemented, which include warped photo attack, cut photo attack and video attack. Therefore each subject contains 12 videos (3 genuine and 9 fake), and the final database contains 600 video clips. Test protocol is provided, which consists of 7 scenarios for a thorough evaluation from all possible aspects. A baseline algorithm is also given for comparison, which explores the high frequency information in the facial region to determine the liveness. We hope such a database can serve as an evaluation platform for future researches in the literature.

Competition on counter measures to 2-D facial spoofing attacks

Spoofing identities using photographs is one of the most common techniques to attack 2-D face recognition systems. There seems to exist no comparative studies of different techniques using the same protocols and data. The motivation behind this competition is to compare the performance of different state-of-the-art algorithms on the same database using a unique evaluation method. Six different teams from universities around the world have participated in the contest. Use of one or multiple techniques from motion, texture analysis and liveness detection appears to be the common trend in this competition. Most of the algorithms are able to clearly separate spoof attempts from real accesses. The results suggest the investigation of more complex attacks.

Face liveness detection by exploring multiple scenic clues

Liveness detection is an indispensable guarantee for reliable face recognition, which has recently received enormous attention. In this paper we propose three scenic clues, which are non-rigid motion, face-background consistency and imaging banding effect, to conduct accurate and efficient face liveness detection. Non-rigid motion clue indicates the facial motions that a genuine face can exhibit such as blinking, and a low rank matrix decomposition based image alignment approach is designed to extract this non-rigid motion. Face-background consistency clue believes that the motion of face and background has high consistency for fake facial photos while low consistency for genuine faces, and this consistency can serve as an efficient liveness clue which is explored by GMM based motion detection method. Image banding effect reflects the imaging quality defects introduced in the fake face reproduction, which can be detected by wavelet decomposition. By fusing these three clues, we thoroughly explore sufficient clues for liveness detection. The proposed face liveness detection method achieves 100% accuracy on Idiap print-attack database and the best performance on self-collected face anti-spoofing database.

Low-resolution face recognition via Simultaneous Discriminant Analysis

Low resolution (LR) is an important issue when handling real world face recognition problems. The performance of traditional recognition algorithms will drop drastically due to the loss of facial texture information in original high resolution (HR) images. To address this problem, in this paper we propose an effective approach named Simultaneous Discriminant Analysis (SDA). SDA learns two mappings from LR and HR images respectively to a common subspace where discrimination property is maximized. In SDA, (1) the data gap between LR and HR is reduced by mapping into a common space; and (2) the mapping is designed for preserving most discriminative information. After that, the conventional classification method is applied in the common space for final decision. Extensive experiments are conducted on both FERET and Multi-PIE, and the results clearly show the superiority of the proposed SDA over state-of-the-art methods.

Regularized Transfer Boosting for Face Detection Across Spectrum

This letter addresses the problem of face detection in multispectral illuminations. Face detection in visible images has been well addressed based on the large scale training samples. For the recently emerging multispectral face biometrics, however, the face data is scarce and expensive to collect, and it is usually short of face samples to train an accurate face detector. In this letter, we propose to tackle the issue of multispectral face detection by combining existing large scale visible face images and a few multispectral face images. We cast the problem of face detection across spectrum into the transfer learning framework and try to learn the robust multispectral face detector by exploring relevant knowledge from visible data domain. Specifically, a novel Regularized Transfer Boosting algorithm named R-TrBoost is proposed, with features of weighted loss objective and manifold regularization. Experiments are performed with face images of two spectrums, 850 nm and 365 nm, and the results show significant improvement on multispectral face detection using the proposed algorithm.

Face Anti-spoofing: Multi-spectral Approach

With the wide applications of face recognition, spoofing attack is becoming a big threat to their security. Conventional face recognition systems usually adopt behavioral challenge-response or texture analysis methods to resist spoofing attacks, however, these methods require high user cooperation and are sensitive to the imaging quality and environments. In this chapter, we present a multi-spectral face recognition system working in VIS (Visible) and NIR (Near Infrared) spectrums, which is robust to various spoofing attacks and user cooperation free. First, we introduce the structure of the system from several aspects including: imaging device, face landmarking, feature extraction, matching, VIS, and NIR sub-systems. Then the performance of the multi-spectral system and each subsystem is evaluated and analyzed. Finally, we describe the multi-spectral image-based anti-spoofing module, and report its performance under photo attacks. Experiments on a spoofing database show the excellent performance of the proposed system both in recognition rate and anti-spoofing ability. Compared with conventional VIS face recognition system, the multi-spectral system has two advantages: (1) By combining the VIS and NIR spectrums, the system can resist VIS photo and NIR photo attacks easily. And users’ cooperation is no longer needed, making the system user friendly and fast. (2) Due to the precise key-point localization, Gabor feature extraction and unsupervised learning, the system is robust to pose, illumination and expression variations. Generally, its recognition rate is higher than the VIS subsystem.

Human Age Estimation Using Ranking SVM

This paper proposes a human age estimation method using Ranking SVM method. Given a face image, most previous methods estimate the human age directly. However, the face images from the same age vary so much that it’s really a difficult problem to estimate the human age accurately. In this work, we adopt an alternative way to estimate the human age. First, the rank relationship of the ages is learned from various face images. Then, the human age is estimated based on the rank relationship and the age information of a reference set. There are two advantages of the proposed method. (i) The rank relationship rather than the absolute human age is learned so that the absolute age estimation problem can be simplified. (ii) The human age is determined based on the rank relationship and the known human age of the reference set so that the face image variations from the same age group can be considered. Experimental results on MORPH and Multi-PIE databases validate the superiority of the rank based human age estimation over some state-of-the-art methods.

Feature space locality constraint for kernel based nonlinear discriminant analysis

Subspace learning is an important approach in pattern recognition. Nonlinear discriminant analysis (NDA), due to its capability of describing nonlinear manifold structure of samples, is considered to be more powerful to undertake classification tasks in image related problems. In kernel based NDA representation, there are three spaces involved, i.e., original data space, implicitly mapped high dimension feature space and the target low dimension subspace. Existing methods mainly focus on the information in original data space to find the most discriminant low dimension subspace. The implicit high dimension feature space plays a role that connects the original space and the target subspace to realize the nonlinear dimension reduction, but the sample geometric structure information in feature space is not involved. In this work, we try to utilize and explore this information. Specifically, the locality information of samples in feature space is modeled and integrated into the traditional kernel based NDA methods. In this way, both the sample distributions in original data space and the mapped high dimension feature space are modeled and more information is expected to be explored to improve the discriminative ability of the subspace. Two algorithms, named FSLC-KDA and FSLC-KSR, are presented. Extensive experiments on ORL, Extended-YaleB, PIE, Multi-PIE and FRGC databases validate the efficacy of the proposed method.