We propose a new deep network structure for unconstrained face recognition. The proposed network integrates several key components together in order to characterize complex data distributions, such as in unconstrained face images. Inspired by recent progress in deep networks, we consider some important concepts, including multi-scale feature learning, dense connections of network layers, and weighting different network flows, for building our deep network structure. The developed network is evaluated in unconstrained face matching, showing the capability of learning complex data distributions caused by face images with various qualities.
Learning multi-scale features is important for accurate face recognition. It was proposed in @cite_18 to apply multi-scale local binary pattern (LBP) on multi-scale Gabor wavelets. LBP features for face images of different scales are extracted in @cite_11 . Multi-scale SIFT proves to be effective @cite_7 , @cite_17 . However, most of these methods are hand-crafted features, in which feature extraction and classification are separate, thus it may not be optimized jointly towards a higher accuracy. Features from different layers are concatenated to represent the face identities in @cite_12 , showing better results than features from one single layer. This multi-level feature extraction inspires us to explore multi-scale features further.
@cite_18 @cite_11 @cite_7 @cite_12 @cite_17
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Local binary pattern (LBP) and its variants are effective descriptors for face recognition. The traditional LBP like features are extracted based on the original pixel or patch values of images. In this paper, we propose to learn the discriminative image filter to improve the discriminant power of the LBP like feature. The basic idea is after the image filtering with the learned filter, the difference of pixel difference vectors (PDVs) between the images from the same person is consistent and the difference between the images from different persons is enlarged. In this way, the LBP like features extracted from the filtered images are considered to be more discriminant than those extracted from the original images. Moreover, a coupled discriminant image filters learning method is proposed to deal with the heterogenous face images matching problem by reducing the feature gap between the heterogeneous images. Experiments on FERET, FRGC and a VIS-NIR heterogeneous face databases validate the effectiveness of our proposed image filter learning method combined with LBP like features. Effective and real-time face detection has been made possible by using the method of rectangle Haar-like features with AdaBoost learning since Viola and Jones' work [12]. In this paper, we present the use of a new set of distinctive rectangle features, called Multi-block Local Binary Patterns (MB-LBP), for face detection. The MB-LBP encodes rectangular regions' intensities by local binary pattern operator, and the resulting binary patterns can describe diverse local structures of images. Based on the MB-LBP features, a boosting-based learning method is developed to achieve the goal of face detection. To deal with the non-metric feature value of MB-LBP features, the boosting algorithm uses multibranch regression tree as its weak classifiers. The experiments show the weak classifiers based on MB-LBP are more discriminative than Haar-like features and original LBP features. Given the same number of features, the proposed face detector illustrates 15 higher correct rate at a given false alarm rate of 0.001 than haar-like feature and 8 higher than original LBP feature. This indicates that MB-LBP features can capture more information about the image structure and show more distinctive performance than traditional haar-like features, which simply measure the differences between rectangles. Another advantage of MB-LBP feature is its smaller feature set, this makes much less training time. Extending recognition to uncontrolled situations is a key challenge for practical face recognition systems. Finding efficient and discriminative facial appearance descriptors is crucial for this. Most existing approaches use features of just one type. Here we argue that robust recognition requires several different kinds of appearance information to be taken into account, suggesting the use of heterogeneous feature sets. We show that combining two of the most successful local face representations, Gabor wavelets and Local Binary Patterns (LBP), gives considerably better performance than either alone: they are complimentary in the sense that LBP captures small appearance details while Gabor features encode facial shape over a broader range of scales. Both feature sets are high dimensional so it is beneficial to use PCA to reduce the dimensionality prior to normalization and integration. The Kernel Discriminative Common Vector method is then applied to the combined feature vector to extract discriminant nonlinear features for recognition. The method is evaluated on several challenging face datasets including FRGC 1.0.4, FRGC 2.0.4 and FERET, with promising results. This paper presents a novel approach to face recognition, named Multi-resolution Histograms of Local Variation Patterns (MHLVP), in which face images are represented as the concatenation of the local spatial histogram of local variation patterns computed from the multi-resolution Gabor features. For a face image with abundant texture and shape information, aGabor feature map(GFM) is computed by convolving the image with each of the forty multi-scale and multi-orientation Gabor filters. Each GFM is then divided into small non-overlapping regions to enhance its shape information, and then Local Binary Pattern (LBP) histograms are extracted for each region and concatenated into a feature histogram to enhance the texture information in the specific GFM. Further more, all of the feature histograms extracted from the forty GFMs are further concatenated into a single feature histogram as the final representation of the given face image. Eventually, the identification is achieved by histogram intersection operation. Our experimental results on FERET face databases show that the proposed method performs terrifically better than the performance of some classical results including the best results in FERET'97.