Disentangled representations have been commonly adopted to Age-invariant Face Recognition (AiFR) tasks. However, these methods reached some limitations with (1) the requirement of large-scale face recognition (FR) training data age labels, which is limited in practice; (2) heavy deep network architectures for high performance; and (3) their evaluations are usually taken place on age-related dat...

Previous research on face restoration often focused repairing specific types of low-quality facial images such as low-resolution (LR) or occluded images. However, in the real world, both above-mentioned forms image degradation coexist. Therefore, it is important to design a model that can repair are LR and simultaneously. This paper proposes multi-scale feature graph generative adversarial netw...

Object detection traditionally requires sliding-window classifier in modern deep learning based approaches. However, both of these approaches tedious configurations bounding boxes. Generally speaking, single-class object is to tell where the is, and how big it is. Traditional methods combine ”where” ”how” subproblems into a single one through overall judgement various scales In view this, we ar...

Empirical studies concerning face recognition suggest that faces may be stored in memory by a few canonical representations. Models of visual perception are based on image representations in cortical area V1 and beyond, which contain many cell layers for feature extraction. Simple, complex and end-stopped cells provide input for line, edge and keypoint detection. Detected events provide a rich,...

This work presents a new approach to automatic face location on gray-scale static images with complex backgrounds. In a "rst stage our technique approximately detects the image positions where the probability of "nding a face is high; during the second stage the location accuracy of the candidate faces is improved and their existence is veri"ed. The experimentation shows that the algorithm perf...

In this paper, we share our experience in designing a convolutional network-based face detector that could handle faces of an extremely wide range of scales. We show that faces with different scales can be modeled through a specialized set of deep convolutional networks with different structures. These detectors can be seamlessly integrated into a single unified network that can be trained end-...

This paper proposes a novel face recognition algorithm based on large-scale supervised hierarchical feature learning. The approach consists of two parts: hierarchical feature learning and large-scale model learning. The hierarchical feature learning searches feature in three levels of granularity in a supervised way. First, face images are modeled by receptive field theory, and the representati...

This paper proposes a framework of face recognition based on the multi-scale local structures of the face image. While some basic tools in this framework are inherited from the SIFT algorithm, this work investigates and contributes to all major steps in the feature extraction and image matching. New approaches to keypoint detection, partial descriptor and insignificant keypoint removal are prop...