中科院计算所视觉信息处理与学习组
中科院计算所视觉信息处理与学习组


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VIPL's paper on face recognition is accepted by the IJCV

Date of publication:2021-11-03      Number of clicks: 10


Recently, one paper on face recognition is accepted by the IJCV. The full name of IJCV is International Journal of Computer Vision, which is one of the top journal on computer vision. Below is a brief introduction of this work.

 

Chunrui Han, Shiguang Shan, Meina Kan, Shuzhe Wu, and Xilin Chen, "Personalized Convolution for Face Recognition," International Journal of Computer Vision (IJCV), Sep. 2021. (Accepted)

 

In conventional convolution, the convolutional kernels for feature extraction are fixed regardless of the input face once the training stage is finished. By contrast, we humans are usually impressed by some unique characteristics of different persons, such as one's blue eyes while another one's naevus at specific location. Inspired by this observation, we propose a personalized convolution method which aims to extract special distinguishing characteristics of each person for more accurate face recognition. Specifically, given a face, we adaptively generate a set of kernels for him/her, named by us ordinary kernel, which is further analytically decomposed into two orthogonal components, i.e., the commonality component and the specialty component. The former characterizes the commonality among subjects which is optimized on a reference set. The latter is the residual part by filtering out the commonality component from the ordinary kernel, so as to capture those special characteristics, named by us personalized kernel. The CNNs with personalized kernels for convolution can highlight those specialty of a person's distinguishing characteristics, leading to better distinguishing of different faces. Additionally, as a by-product, the reference set also facilitates the adaptation of our method to different scenarios by simply selecting faces of a particular population. The framework of our method is below.



Overall, the main contributions of our work lie in three aspects: 1) we propose the personalized convolution to adaptively extract the special distinguishing features of an input face for adaptive and better face recognition; 2) a general framework decomposing one's ordinary kernel into commonality component and specialty component is proposed to generate the personalized kernel for extracting one's special distinguishing features. A reference set is introduced and serves as a strainer to filter out the commonality component from the ordinary kernel while leaving the specialty component as the personalized kernel. Different constitutions of the reference set derive distinct variants of this framework with potential for varying application scenarios; 3) extensive experiments demonstrate the effectiveness of personalized feature extraction for face recognition.



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