A robust and efficient convolutional deep learning framework for age‐invariant face recognition. Issue 3 (23rd December 2019)

• Record Type: Journal Article
• Authors: Yousaf, Adeel; Khan, Muhammad Junaid; Khan, Muhammad Jaleed; Siddiqui, Adil M.; Khurshid, Khurram
• Other Names: Saen Reza Farzipoor guestEditor.; Song Malin guestEditor.; Fisher Ron guestEditor.
• Is Part Of: Expert systems. Volume 37:Issue 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-23
• Subjects: age‐invariant features -- face recognition -- deep learning -- facial ageing -- data augmentation; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12503
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Extensive research has been carried out in the past on face recognition, face detection, and age estimation. However, age‐invariant face recognition (AIFR) has not been explored that thoroughly. The facial appearance of a person changes considerably over time that results in introducing significant intraclass variations, which makes AIFR a very challenging task. Most of the face recognition studies that have addressed the ageing problem in the past have employed complex models and handcrafted features with strong parametric assumptions. In this work, we propose a novel deep learning framework that extracts age‐invariant and generalized features from facial images of the subjects. The proposed model trained on facial images from a minor part (20–30%) of lifespan of subjects correctly identifies them throughout their lifespan. A variety of pretrained 2D convolutional neural networks are compared in terms of accuracy, time, and computational complexity to select the most suitable network for AIFR. Extensive experimental results are carried out on the popular and challenging face and gesture recognition network ageing dataset. The proposed method achieves promising results and outperforms the state‐of‐the‐art AIFR models by achieving an accuracy of 99%, which proves the effectiveness of deep learning in facial ageing research.