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Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work well and how to design a 'good' architecture. The existing works tend to focus on reporting CNN architectures that work well for face recognition rather than investigate the reason. In this work, we conduct an extensive evaluation...
After intensive research, heterogenous face recognition is still a challenging problem. The main difficulties are owing to the complex relationship between heterogenous face image spaces. The heterogeneity is always tightly coupled with other variations, which makes the relationship of heterogenous face images highly nonlinear. Many excellent methods have been proposed to model the nonlinear relationship,...
Subspace learning is an important technique to enhance the discriminative ability of feature representation and reduce the dimension to improve its efficiency. Due to limited training samples and the usual high-dimensional feature, subspace learning always suffers from overfitting problem, which affects its generalization performance. One possible method is to introduce prior information as a regularizer...
Recently, pedestrian attributes like gender, age and clothing etc., have been used as soft biometric traits for recognizing people. Unlike existing methods that assume the independence of attributes during their prediction, we propose a multi-label convolutional neural network (MLCNN) to predict multiple attributes together in a unified framework. Firstly, a pedestrian image is roughly divided into...
Face detection has drawn much attention in recent decades since the seminal work by Viola and Jones. While many subsequences have improved the work with more powerful learning algorithms, the feature representation used for face detection still can't meet the demand for effectively and efficiently handling faces with large appearance variance in the wild. To solve this bottleneck, we borrow the concept...
Various hand-crafted features and metric learning methods prevail in the field of person re-identification. Compared to these methods, this paper proposes a more general way that can learn a similarity metric from image pixels directly. By using a "siamese" deep neural network, the proposed method can jointly learn the color feature, texture feature and metric in a unified framework. The...
LBP is an effective descriptor for face recognition. LBP encodes the ordinal relationship between the neighborhood samplings and the central one to obtain robust face representation. However, additional information like the difference among neighboring pixels, which may be helpful for face recognition, is ignored. On the other hand, gradient information which enhances the edge response and suppresses...
In recent years, large-scale video search and mining has been an active research area. Exploring the trajectory of pedestrian of interest in non-overlapping multi-camera network, namely the trajectory mining, is very useful for visual surveillance and criminal investigation. The trajectory mentioned in our work describes the transition of pedestrian among cameras from a macroscopic perspective which...
Attributes are helpful to infer high-level semantic knowledge of pedestrians, thus improving the performance of pedestrian tracking, retrieval, re-identification, etc. However, current pedestrian databases are mainly for the pedestrian detection or tracking application, and semantic attribute annotations related to pedestrians are rarely provided. In this paper, we construct an Attributed Pedestrians...
Spoofing attacks mainly include printing artifacts, electronic screens and ultra-realistic face masks or models. In this paper, we propose a component-based face coding approach for liveness detection. The proposed method consists of four steps: (1) locating the components of face; (2) coding the low-level features respectively for all the components; (3) deriving the high-level face representation...
As a crucial security problem, anti-spoofing in biomet-rics, and particularly for the face modality, has achieved great progress in the recent years. Still, new threats arrive in form of better, more realistic and more sophisticated spoofing attacks. The objective of the 2nd Competition on Counter Measures to 2D Face Spoofing Attacks is to challenge researchers to create counter measures effectively...
Face recognition, which is security-critical, has been widely deployed in our daily life. However, traditional face recognition technologies in practice can be spoofed easily, for example, by using a simple printed photo. In this paper, we propose a novel face liveness detection approach to counter spoofing attacks by recovering sparse 3D facial structure. Given a face video or several images captured...
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...
Recent state-of-the-art algorithms have achieved good performance on normal pedestrian detection tasks. However, pedestrian detection in crowded scenes is still challenging due to the significant appearance variation caused by heavy occlusions and complex spatial interactions. In this paper we propose a unified probabilistic framework to globally describe multiple pedestrians in crowded scenes in...
Heterogeneous Face Recognition (HFR) refers recognition of face images captured in different modalities, e.g. Visual (VIS), near infrared (NIR) and thermal infrared (TIR). Although heterogeneous face images of a given person differ by pixel values, the identity of the face should be classified as the same. This paper focuses on NIR-VIS HFR. Light Source Invariant Features (LSIFs) are derived to extract...
This paper presents an occlusion robust image representation method and apply it to face recognition. Inspired from the recent work [15], we propose a Gabor phase difference representation for occlusion robust face recognition. Based on the good ability of Gabor filters to capture image structure and the robustness to image occlusion shown in this paper, Gabor phase features are expected to be discriminative...
Coupled spectral regression (CSR) is an effective framework for heterogeneous face recognition (e.g., visual light (VIS) vs. near infrared (NIR)). CSR aims to learn different projections for different face modalities respectively to find a common subspace where the samples of different modalities from the same class are as close as possible. In original CSR, the projection for one modality is supposed...
Face antispoofing has now attracted intensive attention, aiming to assure the reliability of face biometrics. We notice that currently most of face antispoofing databases focus on data with little variations, which may limit the generalization performance of trained models since potential attacks in real world are probably more complex. In this paper we release a face antispoofing database which covers...
Feature selection is an important issue in pattern recognition. In face recognition, one of the state-of-the-art methods is that some feature selection methods (e.g., AdaBoost) are first utilized to select the most discriminative features and then the subspace learning methods (e.g., LDA) are further applied to learn the discriminant subspace for classification. However, in these methods, the objective...
We propose a framework for efficient storing and scalable browsing of surveillance video based on video synopsis. Our framework employs a novel synopsis analysis scheme named Detail-based video synopsis to generate a set of object flags to store and browse surveillance video synopsis. The main contributions of our work are: 1) highlighting important contents of surveillance video; 2) improving the...
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