Yandong Wen

SphereFace: Deep Hypersphere Embedding for Face Recognition

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this end, we propose the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss can be viewed as imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also lie on a manifold. Moreover, the size of angular margin can be quantitatively adjusted by a parameter m. We further derive specific m to approximate the ideal feature criterion. Extensive analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace Challenge 1 show the superiority of A-Softmax loss in FR tasks.

KCRC-LCD: Discriminative kernel collaborative representation with locality constrained dictionary for visual categorization

Abstract We consider the image classification problem via kernel collaborative representation classification with locality constrained dictionary (KCRC-LCD). Specifically, we propose a kernel collaborative representation classification (KCRC) approach in which kernel method is used to improve the discrimination ability of collaborative representation classification (CRC). We then measure the similarities between the query and atoms in the global dictionary in order to construct a locality constrained dictionary (LCD) for KCRC. In addition, we discuss several similarity measure approaches in LCD and further present a simple yet effective unified similarity measure whose superiority is validated in experiments. There are several appealing aspects associated with LCD. First, LCD can be nicely incorporated under the framework of KCRC. The LCD similarity measure can be kernelized under KCRC, which theoretically links CRC and LCD under the kernel method. Second, KCRC-LCD becomes more scalable to both the training set size and the feature dimension. Example shows that KCRC is able to perfectly classify data with certain distribution, while conventional CRC fails completely. Comprehensive experiments on widely used public datasets also show that KCRC-LCD is a robust discriminative classifier with both excellent performance and good scalability, being comparable or outperforming many other state-of-the-art approaches.

Structured occlusion coding for robust face recognition

Occlusion in face recognition is a common yet challenging problem. While sparse representation based classification (SRC) has been shown promising performance in laboratory conditions (i.e. noiseless or random pixel corrupted), it performs much worse in practical scenarios. In this paper, we consider the practical face recognition problem, where the occlusions are predictable and available for sampling. We propose the structured occlusion coding (SOC) to address occlusion problems. The structured coding here lies in two folds. On one hand, we employ a structured dictionary for recognition. On the other hand, we propose to use the structured sparsity in this formulation. Specifically, SOC simultaneously separates the occlusion and classifies the image. In this way, the problem of recognizing an occluded image is turned into seeking a structured sparse solution on occlusion-appended dictionary. In order to construct a well-performing occlusion dictionary, we propose an occlusion mask estimating technique via locality constrained dictionary (LCD), showing striking improvement in occlusion sample. On a category-specific occlusion dictionary, we replace l1 norm sparsity with the structured sparsity which is shown more robust, further enhancing the robustness of our approach. Moreover, SOC achieves significant improvement in handling large occlusion in real world. Extensive experiments are conducted on public data sets to validate the superiority of the proposed algorithm.

Jointly Learning Non-negative Projection and Dictionary with Discriminative Graph Constraints for Classification

Sparse coding with dictionary learning (DL) has shown excellent classification performance. Despite the considerable number of existing works, how to obtain features on top of which dictionaries can be better learned remains an open and interesting question. Many current prevailing DL methods directly adopt well-performing crafted features. While such strategy may empirically work well, it ignores certain intrinsic relationship between dictionaries and features. We propose a framework where features and dictionaries are jointly learned and optimized. The framework, named joint non-negative projection and dictionary learning (JNPDL), enables interaction between the input features and the dictionaries. The non-negative projection leads to discriminative parts-based object features while DL seeks a more suitable representation. Discriminative graph constraints are further imposed to simultaneously maximize intra-class compactness and inter-class separability. Experiments on both image and image set classification show the excellent performance of JNPDL by outperforming several state-of-the-art approaches.

A kernel-based l 2 norm regularized least square algorithm for vehicle logo recognition

We consider the problem of automatically recognizing the vehicle logos from the frontal views with varying illumination, as well as certain corruption. To better address the problem, a kernel-based l2 norm regularized least square (RLS) algorithm is proposed in the paper. Kernel technique is smoothly combined with the l2 norm RLS algorithm to enhance the performance of vehicle logo recognition (VLR). As an extension, the improvement of dictionary is also considered. A simple mechanism of constructing an adaptive online dictionary has been presented and experimented. Experimental results show that our proposed algorithm outperforms the original l2 norm RLS algorithm and the l1 norm based algorithms.

Multi-kernel collaborative representation for image classification

We consider the image classification problem via multiple kernel collaborative representation (MKCR). We generalize the kernel collaborative representation based classification to a multi-kernel framework where multiple kernels are jointly learned with the representation coefficients. The intrinsic idea of multiple kernel learning is adopted in our MKCR model. Experimental results show MKCR converges within reasonable iterations and achieves state-of-the-art performance.

Dictionary construction for sparse representation classification: A novel cluster-based approach

There has been a rapid development in sparse representation classification (SRC) since it came out. Most previous work on dictionary improvement was to enhance the classification performance by modifying the dictionary representation structure while this paper concentrates on the reduction of dictionary length with nearly no sacrifice in classification accuracy. A novel cluster-based dictionary construction approach for SRC is proposed in this paper. Both cluster technique and clustering evaluation index are introduced to help construct an optimal dictionary for better classification performance. Results of experiments have verified that the new dictionary does not lose discrimination ability while its running time is greatly reduced. Most importantly, its robustness is also preserved.