The 2013 face recognition evaluation in mobile environment

Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of three types of features: local binary patterns, Gabor wavelet responses including Gabor phases, and color information. The best results are obtained from UNILJ-ALP, which fused several image representations and feature types, and UC-HU, which learns optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources. © 2013 IEEE

Advanced Biometric Technologies: Emerging Scenarios and Research Trends

Biometric systems are the ensemble of devices, procedures, and algorithms for the automatic recognition of individuals by means of their physiological or behavioral characteristics. Although biometric systems are traditionally used in high-security applications, recent advancements are enabling the application of these systems in less-constrained conditions with non-ideal samples and with real-time performance. Consequently, biometric technologies are being increasingly used in a wide variety of emerging application scenarios, including public infrastructures, e-government, humanitarian services, and user-centric applications. This chapter introduces recent biometric technologies, reviews emerging scenarios for biometric recognition, and discusses research trends

Kinship verification using color features and extreme learning machine

Abstract Kinship verification from faces is a challenging task that is attracting an increasing attention in the recent years. The proposed methods so far are not robust enough to predict the kin between persons via facial appearance only. The initial studies using deep convolutional neural networks (CNN) have not shown their full potential as well, mainly due to limited training data. To mitigate this problem, we propose a new approach to kinship verification based on color features and extreme learning machines (ELM). While ELM aims to deal with small size training sets, color features are proven to provide significant enhancement over gray-scale counterparts. We evaluate our proposed method on three benchmark and publicly available kinship databases, namely KinFaceW-I, KinFaceW-II and TSKinFace. The obtained results compares favorably against some state-of-the-art methods including those based on deep learning

3D face verification across pose based on euler rotation and tensors

Abstract In this paper, we propose a new approach for 3D face verification based on tensor representation. Face challenges, such as illumination, expression and pose, are modeled as a multilinear algebra problem where facial images are represented as high order tensors. Particularly, to account for head pose variations, several pose scans are generated from a single depth image using Euler transformation. Multi-bloc local phase quantization (MB-LPQ) histogram features are extracted from depth face images and arranged as a third order tensor. The dimensionality of the tensor is reduced based on the higher-order singular value decomposition (HOSVD). HOSVD projects the input tensor in a new subspace in which the dimension of each tensor mode is reduced. To discriminate faces of different persons, we utilize the Enhanced Fisher Model (EFM). Experimental evaluations on CASIA-3D database, which contains large head pose variations, demonstrate the effectiveness of the proposed approach. A verification rate of 98.60% is obtained

Fuzzy reasoning model to improve face illumination invariance

Abstract Enhancing facial images captured under different lighting conditions is an important challenge and a crucial component in the automatic face recognition systems. This work tackles illumination variation challenge by proposing a new face image enhancement approach based on Fuzzy theory. The proposed Fuzzy reasoning model generates an adaptive enhancement which corrects and improves non-uniform illumination and low contrasts. The FRM approach has been assessed using four blind-reference image quality metrics supported by visual assessment. A comparison to six state-of-the-art methods has also been provided. Experiments are performed on four public data sets, namely Extended Yale-B, Mobio, FERET and Carnegie Mellon University Pose, Illumination, and Expression, showing very interesting results achieved by our approach

On the usefulness of color for kinship verification from face images

Abstract Automatic kinship verification from faces aims to determine whether two persons have a biological kin relation or not by comparing their facial attributes. This is a challenging research problem that has recently received lots of attention from the research community. However, most of the proposed methods have mainly focused on analyzing only the luminance (i.e. gray-scale) of the face images, hence discarding the chrominance (i.e. color) information which can be a useful additional cue for verifying kin relationships. This paper investigates for the first time the usefulness of color information in the verification of kinship relationships from facial images. For this purpose, we extract joint color-texture features to encode both the luminance and the chrominance information in the color images. The kinship verification performance using joint color-texture analysis is then compared against counterpart approaches using only gray-scale information. Extensive experiments using different color spaces and texture features are conducted on two benchmark databases. Our results indicate that classifying color images consistently shows superior performance in three different color spaces

Multilinear Side-Information based Discriminant Analysis for face and kinship verification in the wild

Abstract This paper presents a new approach for face and kinship verification under unconstrained environments. The proposed approach is based on high order tensor representation of face images. The face tensor is built based on local descriptors extracted at multiscales. Besides, we formulate a novel Multilinear Side-Information based Discriminant Analysis (MSIDA) to handle the weakly supervised multilinear subspace projection and classification. Using only the weak label information, MSIDA projects the input face tensor in a new subspace in which the discrimination is improved and the dimension of each tensor mode is reduced simultaneously. Experimental evaluation on four challenging face databases (LFW, Cornell KinFace, UB KinFace and TSKinface) demonstrates that the proposed approach significantly outperforms the current state of the art

Kinship verification from facial images and videos:human versus machine

Abstract Automatic kinship verification from facial images is a relatively new and challenging research problem in computer vision. It consists in automatically determining whether two persons have a biological kin relation by examining their facial attributes. In this work, we compare the performance of humans and machines in kinship verification tasks. We investigate the state-of-the-art methods in automatic kinship verification from facial images, comparing their performance with the one obtained by asking humans to complete an equivalent task using a crowdsourcing system. Our results show that machines can consistently beat humans in kinship classification tasks in both images and videos. In addition, we study the limitations of currently available kinship databases and analyzing their possible impact in kinship verification experiment and this type of comparison