Integrating generalized domain adaptation and Fisher discriminative learning: A unified framework for face recognition with single sample per person

In this paper, an enhanced discriminative feature learning (EDFL) method is proposed to address single sample per person (SSPP) face recognition. With a separate auxiliary dataset, EDFL integrates Fisher discriminative learning and domain adaptation into a unified framework. The separate auxiliary dataset and the gallery/probe dataset are from two different domains (named source and target domains respectively) and have different data distributions. EDFL is modeled to transfer the discriminative knowledge learned from the source domain to the target domain for classification. Since the gallery set with SSPP contains scarce number of samples, it is hard to accurately represent the data distribution of the target domain, which hinders the adaptation effect. To overcome this problem, the generalized domain adaption (GDA) method is proposed to realize good overall domain adaptation when one domain contains limited samples. GDA considers the both global and local domain adaptation effect at the same time. Further, to guarantee that the learned domain adaptation components are optimal for discriminative learning, the domain adaptation and Fisher discriminant model learning are unified into a single framework and an efficient algorithm is designed to optimize them. The effectiveness of the proposed approach is demonstrated by extensive evaluation and comparison with some state-of-the-art methods.

Training Set Enlargement Using Binary Weighted Interpolation Maps for the Single Sample per Person Problem in Face Recognition

We propose a method of enlarging the training dataset for a single-sample-per-person (SSPP) face recognition problem. The appearance of the human face varies greatly, owing to various intrinsic and extrinsic factors. In order to build a face recognition system that can operate robustly in an uncontrolled, real environment, it is necessary for the algorithm to learn various images of the same person. However, owing to limitations in the collection of facial image data, only one sample can typically be obtained, causing difficulties in the performance and usability of the method. This paper proposes a method that analyzes the changes in pixels in face images associated with variations by extracting the binary weighted interpolation map (B-WIM) from neutral and variational images in the auxiliary set. Then, a new variational image for the query image is created by combining the given query (neutral) image and the variational image of the auxiliary set based on the B-WIM. As a result of performing facial recognition comparison experiments on SSPP training data for various facial-image databases, the proposed method shows superior performance compared with other methods.

Auxiliary Dictionary of Diversity Learning for Face Recognition with a Single Sample Per Person

Face recognition for a single sample per person is challenging due to the lack of sufficient sample information. However, using generic training set to learn an auxiliary dictionary is an effective way to alleviate this problem. Considering generic training sample of diversity, we proposed an algorithm of auxiliary dictionary of diversity learning (ADDL). We first produced virtual face images by mirror images, square block occlusion and grey transform, and then learned an auxiliary dictionary of diversity using a designed objective function. Considering patch-based method can reduce the influence of variations, we seek extended sparse representation with l2-minimization for each probe patch. Experimental results in the CMUPIE, Extended Yale B and LFW datasets demonstrate that ADDL performs better than other related algorithms.

Improved Single Sample Per Person Face Recognition via Enriching Intra-Variation and Invariant Features

Face recognition using a single sample per person is a challenging problem in computer vision. In this scenario, due to the lack of training samples, it is difficult to distinguish between inter-class variations caused by identity and intra-class variations caused by external factors such as illumination, pose, etc. To address this problem, we propose a scheme to improve the recognition rate by both generating additional samples to enrich the intra-variation and eliminating external factors to extract invariant features. Firstly, a 3D face modeling module is proposed to recover the intrinsic properties of the input image, i.e., 3D face shape and albedo. To obtain the complete albedo, we come up with an end-to-end network to estimate the full albedo UV map from incomplete textures. The obtained albedo UV map not only eliminates the influence of the illumination, pose, and expression, but also retains the identity information. With the help of the recovered intrinsic properties, we then generate images under various illuminations, expressions, and poses. Finally, the albedo and the generated images are used to assist single sample per person face recognition. The experimental results on Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), Celebrities in Frontal-Profile (CFP) and other face databases demonstrate the effectiveness of the proposed method.