A novel active shape model-based DeepNeural network for age invariance face recognition

人工智能 模式识别(心理学) 卷积神经网络 计算机科学 面部识别系统 降维 面子(社会学概念) 特征提取 维数之咒 不变(物理) 数学 社会科学 社会学 数学物理
作者
Ashutosh Dhamija,R. B. Dubey
出处
期刊:Journal of Visual Communication and Image Representation [Elsevier BV]
卷期号:82: 103393-103393 被引量:4
标识
DOI:10.1016/j.jvcir.2021.103393
摘要

Scientific efforts have expanded in age-invariant face recognition (AIFR). Matching faces of large age difference is, therefore, a problem, mostly because of a substantial disparity in the appearance of both young and old age. Owing to age, both the appearance and shape of the face are impaired, making recognition of the face the most challenging task. In recent years, AIFR has become a very common and demanding task. The set of feature extraction and classification algorithm is of prime importance in this field. As the numbers of features obtained from the datasets are large, there is a need to introduce a dimensionality reduction method to map high dimensionality feature space to low variance filter to form the final integrated face age model to be used in the classification process. In this paper, we introduced a novel concept of an improved Active Shape Model (ASM) in conjunction with a specially designed 7-layered Convolutional Neural Network (CNN) in order to accomplish a combination of feature extraction and classification in a single unit. The study approach involves conducting extensive experiments to evaluate the proposed system's performance using three standard datasets: FG-NET, LAG, and CACD. The results reveal that the proposed method outperforms state-of-the-art approaches and achieves excellent accuracy in face recognition across age. The maximum accuracies achieved by demonstrated ASM-CNN methodology for FG-NET, LAG, and CACD databases are 95.02%, 91.76 % and 99.4 % respectively.
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