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Many recent variational optical flow methods are not robust for illumination variance, and they only consider local image relation in terms of illumination. In this paper, we propose a new efficient illumination-invariance total variation optical flow method called the weighted regularization transform, which uses and optimizes the Weber’s Law. Our method exploits unequal probability as the weight that has non-local information to estimate stable optical flow despite illumination changes. The proposed method uses a coarse-to-fine pyramid model to reduce the influence on the data term from illumination. Then, an energy optimization procedure is introduced to constrain the minimization of the data term with the non-local regularization. Experimentation with the proposed method has been performed on three optical flow datasets and a face liveness detection database, which have challenging illumination variations, and the results demonstrate that the proposed method is quite robust with respect to variations in illumination.

The single-image super-resolution techniques (SISR) have been significantly promoted by deep networks. However, the storage and computation complexities of deep models increase dramatically alongside with the reconstruction performance. This paper proposes a densely connected recursive network (DCRN) to trade off the performance and complexity. We introduce an enhanced dense unit by removing the batch normalization (BN) layers and employing the squeeze-and-excitation (SE) structure. A recursive architecture is also adopted to control the parameters of deep networks. Moreover, a de-convolution based residual learning method is proposed to accelerate the residual feature extraction process. The experimental results validate the efficiency of the proposed approach.

Illumination problem is still a bottleneck of robust face recognition system, which demands extracting illumination invariant features. In this field, existing works only consider the variations caused by lighting direction or magnitude (denoted as homogeneous lighting), but the effect of spectral wavelength is always ignored and thus existing illumination invariant descriptors have its limitation on processing face images under different spectral wavelengths (denoted as heterogeneous lighting). We propose a novel gradient based descriptor, namely Logarithm Gradient Histogram (LGH), which takes the illumination direction, magnitude and the spectral wavelength together into consideration, so that it can handle both homogeneous and heterogeneous lightings. Our proposal contributes in three-folds: (1) we incorporate LMSN-LoG filter to eliminate the lighting effect of each image and extract two illumination invariant components, namely logarithm gradient orientation (LGO) and logarithm gradient magnitude (LGM); (2) we propose an effective post-processing strategy to make our model tolerant to noise and generate a histogram representation to integrate both LGO and LGM; (3) we present solid theoretical analysis on the illumination invariant properties of our proposed descriptors. Extensive experimental results on CMU-PIE, Extended YaleB, FRGC and HFB databases are reported to verify the effectiveness of our proposed model. HighlightsTwo illumination invariant components, namely logarithm gradient orientation (LGO) and logarithm gradient magnitude (LGM), are extracted.An effective post-processing strategy is proposed to integrate both LGO and LGM, generating the logarithm gradient histogram (LGH).Solid theoretical analysis on the illumination invariant properties of the proposed descriptors is presented.Competitive results are reported, both in homogeneous and heterogeneous lighting conditions.

Age estimation based on unconstrained face images remains a challenging problem in computer vision and pattern recognition. We address this by ensembling part-based features and designing a cost sensitive loss to overcome the general imbalance data in age estimation. Specifically, we treat age estimation as a multi-class classification problem and mainly make two contributions: (i) We present a Part-based Convolutional Network(PCN) for age estimation to extract regional features instead of the holistic ones, which can preserve more discriminative features in facial regions like forehead, canthus, cheek, jaw, etc; (ii) A balanced loss for multi-class classification is designed to handle the extremely imbalance age distribution. The loss pays more attention to the hard examples, and can automatically adjust the weights of samples according to their contribution. State-of-the-art performance was achieved on FG-NET, MORPH and CACD databases, validating the effectiveness of the proposed approach.

It is generally known that the illumination could seriously affect the performance of face analysis algorithms. Moreover, in most practical applications, the illumination is usually uncontrolled. A number of methods have been put forward to tackle the problem of illumination variations in face images, but they always only work on facial region and need to segment faces in advance. Furthermore, many illumination processing methods only demonstrate on grayscale images and require strict alignment of face images, resulting in limited applications in the real world. In this paper, we propose a face image illumination processing method based on the Generative Adversarial Network (GAN) with dual triplet loss. Through considering the inter-domain similarity and intra-domain difference between the generated images and the real images, we put forward the dual triplet loss. At the same time, we introduce the self-similarity constraint of the images in the target illumination field. Experiments on the CMU Multi-PIE face datasets demonstrate that the proposed method preserve the facial details well when relighting. The experiment of 3D face reconstruction also verifies the effectiveness of the proposed method.

Face recognition is an increasingly popular technology for user authentication. However, face recognition is susceptible to spoofing attacks. Therefore, a reliable way to detect malicious attacks is crucial to the robustness of the face recognition system. This paper describes a new approach to utilizing light field camera for defending spoofing face attacks, like (warped) printed 2D facial photos and high-definition tablet images. The light field camera is a sensor that can record the directions as well as the colors of incident rays. Needing only one snapshot, multiple refocused images can be generated. In the proposed method, three kinds of features extracted from a pair of refocused images are extracted to discriminate fake faces and real faces. To verify the performance, we build a light field photograph databases and conduct experiments. Experimental results reveal that the employed features can achieve remarkable anti-spoofing accuracy under different types of spoofing attacks.

Person re-identification (re-id) aims at matching person images across multiple surveillance cameras. Currently, most re-id systems highly rely on color cues, which are only effective in good illumination conditions, but fail in low lighting conditions. However, for security issues, it is very important to conduct surveillance in low lighting conditions. To remedy this problem, we propose using depth cameras to perform surveillance in dark places, while using traditional RGB cameras in bright places. Such a heterogeneous camera network brings a challenge to match images across depth and RGB cameras. In this paper, we mine the correlation between two heterogeneous cues (depth and RGB) on both feature-level and transformation-level. As such, depth-based features and RGB-based features are transformed to the same space, which alleviates the problem of cross-modality matching between depth and RGB cameras. Experimental results on two benchmark heterogeneous person re-id datasets show the effectiveness of our method.