Face recognition has attracted increasing attention due to its wide range of applications, but it is still challenging when facing large variations in the biometric data characteristics. Lenslet light field cameras have recently come into prominence to capture rich spatio-angular information, thus offering new possibilities for advanced biometric recognition systems. This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to learn both texture and angular dynamics in sequence using convolutional representations; this is a novel recognition framework that has never been proposed before for either face recognition or any other visual recognition task. The proposed double-deep learning framework includes a long short-term memory (LSTM) recurrent network whose inputs are VGG-Face descriptions that are computed using a VGG-Very-Deep-16 convolutional neural network (CNN). The VGG-16 network uses different face viewpoints rendered from a full light field image, which are organised as a pseudo-video sequence. A comprehensive set of experiments has been conducted with the IST-EURECOM light field face database, for varied and challenging recognition tasks. Results show that the proposed framework achieves superior face recognition performance when compared to the state-of-the-art.
翻译:面部识别因其应用范围广泛而引起越来越多的关注,但在面临生物鉴别数据特性的巨大差异时,这种认识仍然具有挑战性。 Lenslet光场照相机最近成为突出人物,以捕捉丰富的spatio-agle信息,从而为先进的生物鉴别识别系统提供了新的可能性。本文件提议了一个双深的spatio-agle学习框架,用于光场面部识别,它能够利用相形色色色的表达方式,在顺序上学习质谱和角动态;这是一个新颖的识别框架,以前从未为面部识别或任何其他视觉识别任务提出过。拟议的双深学习框架包括一个长期的短期记忆(LSTM)经常网络,其投入是VGG-Very-Dep-16 脉冲网络(CNN)的VGGG-Very-Dep-16 脉冲神经网络(CNN)计算出来的VGGG-VGIG-16。网络使用从全光场图像中得出的不同面部观点,该图像是假视序列。与IST-ERECOM光场面部表面识别数据库一起进行全面的实验。结果显示,在与具有差异和具有挑战性的确认状态时,拟议框架取得了高度。