Fully automatic face normalization and single sample face recognition in unconstrained environments. (1st April 2016)

• Record Type: Journal Article
• Authors: Haghighat, Mohammad; Abdel-Mottaleb, Mohamed; Alhalabi, Wadee
• Is Part Of: Expert systems with applications. Volume 47(2016)
• Page Start: 23
• Page End: 34
• Publication Date: 2016-04-01
• Subjects: Face recognition in-the-wild -- Pose-invariance -- Frontal face synthesizing -- Feature-level fusion -- Canonical correlation analysis -- Active appearance models; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.10.047
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We present a fully automatic face normalization and recognition system. It normalizes the face images for both in-plane and out-of-plane pose variations. The performance of AAM fitting is improved using a novel initialization technique. HOG and Gabor features are fused using CCA to have more discriminative features. The proposed system recognizes non-frontal faces using only a single gallery sample. Abstract: Single sample face recognition have become an important problem because of the limitations on the availability of gallery images. In many real-world applications such as passport or driver license identification, there is only a single facial image per subject available. The variations between the single gallery face image and the probe face images, captured in unconstrained environments, make the single sample face recognition even more difficult. In this paper, we present a fully automatic face recognition system robust to most common face variations in unconstrained environments. Our proposed system is capable of recognizing faces from non-frontal views and under different illumination conditions using only a single gallery sample for each subject. It normalizes the face images for both in-plane and out-of-plane pose variations using an enhanced technique based on active appearance models (AAMs). We improve the performance of AAM fitting, not only by training it with in-the-wild images and using a powerful optimization technique, but also by initializing the AAM with estimates of the locations of the facial landmarks obtained by a method based on flexible mixture of parts. The proposed initialization technique results in significant improvement of AAM fitting to non-frontal poses and makes the normalization process robust, fast and reliable. Owing to the proper alignment of the face images, made possible by this approach, we can use local feature descriptors, such as Histograms of Oriented Gradients (HOG), for matching. The use of HOG features makes the system robust against illumination variations. In order to improve the discriminating information content of the feature vectors, we also extract Gabor features from the normalized face images and fuse them with HOG features using Canonical Correlation Analysis (CCA). Experimental results performed on various databases outperform the state-of-the-art methods and show the effectiveness of our proposed method in normalization and recognition of face images obtained in unconstrained environments.