Age Invariant Face Recognition Based on DCT Feature Extraction and Kernel Fisher Analysis

Abstract

The aim of this paper is to examine the effectiveness of combining three popular tools used in pattern recognition, which are the Active Appearance Model (AAM), the two-dimensional discrete cosine transform (2D-DCT), and Kernel Fisher Analysis (KFA), for face recognition across age variations. For this purpose, we first used AAM to generate an AAM-based face representation; then, we applied 2D-DCT to get the descriptor of the image; and finally, we used a multiclass KFA for dimension reduction. Classification was made through a K-nearest neighbor classifier, based on Euclidean distance. Our experimental results on face images, which were obtained from the publicly available FG-NET face database, showed that the proposed descriptor worked satisfactorily for both face identification and verification across age progression.

Extreme Learning Machine-Based Age-Invariant Face Recognition With Deep Convolutional Descriptors

“…However, discriminative methods (Li et al (2017)), (Boussaad et al (2016)), (Sajid et al (2018a)), and (Sajid et al (2018b)) focus particularly on the choice of discriminatory features and metric learning that are invariant over time. Lately, another category can be added, it includes deep-learning based methods (Wang et al (2018)), (El Khiyari et al (2016)), (El Khiyari et al (2017)), (Sajid et al (2018c)), (Zhao et al (2019)), and (Ni et al (2019)).…”