Three-dimensional face recognition under expression variation

Abstract

In this paper, we introduce a fully automatic framework for 3D face recognition under expression variation. For 3D data preprocessing, an improved nose detection method is presented. The small pose is corrected at the same time. A new facial expression processing method which is based on sparse representation is proposed subsequently. As a result, this framework enhances the recognition rate because facial expression is the biggest obstacle for 3D face recognition. Then, the facial representation, which is based on the dual-tree complex wavelet transform (DT-CWT), is extracted from depth images. It contains the facial information and six subregions' information. Recognition is achieved by linear discriminant analysis (LDA) and nearest neighbor classifier. We have performed different experiments on the Face Recognition Grand Challenge database and Bosphorus database. It achieves the verification rate of 98.86% on the all vs. all experiment at 0.1% false acceptance rate (FAR) in the Face Recognition Grand Challenge (FRGC) and 95.03% verification rate on nearly frontal faces with expression changes and occlusions in the Bosphorus database.

Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

“…The study [17] is a representative work of region based face recognition methods. The study proposed the use of facial representation based on dual-tree complex wavelet transform (DT-CWT) and six subregions.…”

“…Therefore, pose invariant face recognition using 3D models is proving to be promising especially in case of in-depth pose variations along x-, y-, and z-axis under unconstrained acquisition scenarios of real world [15]. Encouraged by these lines of evidence, many 3D face recognition approaches have been evolved and experimented in the last few years as given in the work of Bowyer et al [16] and the literature reviews [17][18][19][20]. The existing 3D face recognition approaches can be grouped into holistic, local feature-based, and hybrid domains [20].…”

“…These databases carry pose and expression variations and are commonly utilized for developing 3D face recognition algorithms. For example, the algorithms presented in state-of-the-art studies [17,[41][42][43] employed FRGC v2.0 database while those presented in Refs. [19,26,28,[44][45][46] are based on both of FRGC v2.0 and GavabDB databases.…”

Assessment of the effect of variations in Eye Blinks on a face recognition algorithm

“…Currently face recognition has been used as a secure means of biometric authentication. But this system is usually designed for co-operative user and indoor applications since such systems get compromised in accuracy due to factors like variations in occlusions, illumination, cosmetics, skin tone, pose [1], age, facial expressions [2] etc. in the face images acquired.…”

Noise-resistant network: a deep-learning method for face recognition under noise

“…However, in some uncontrolled conditions, including varying illumination, poses, facial expressions, and noise, the performance of face recognition system would be dramatically affected. Extensive works have been carried out towards the illumination, pose, and expression problems and also get some excellent results [10][11][12]. But when it comes to the noisy images, the recognition accuracy of most approaches would drop significantly.…”