Regional adaptive affinitive patterns (RADAP) with logical operators for facial expression recognition. Issue 5 (18th March 2019)

• Record Type: Journal Article
• Authors: Mandal, Murari; Verma, Monu; Mathur, Sonakshi; Vipparthi, Santosh Kumar; Murala, Subrahmanyam; Kranthi Kumar, Deveerasetty
• Is Part Of: IET image processing. Volume 13:Issue 5(2019)
• Page Start: 850
• Page End: 861
• Publication Date: 2019-03-18
• Subjects: emotion recognition -- image classification -- face recognition -- image representation -- feature extraction; positional adaptive thresholds -- multidistance magnitude features -- intra‐class variations -- irregular illumination variation -- cross‐distance co‐occurrence relations -- logical operators -- RADAP features -- stable invariant features -- illumination invariant features -- minute expression features -- Indian spontaneous expression database -- regional adaptive affinitive patterns -- facial expressions -- human emotional state -- automated facial expression recognition system -- human behaviour analysis -- robust feature descriptor; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5683
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Automated facial expression recognition plays a significant role in the study of human behaviour analysis. In this study, the authors propose a robust feature descriptor named regional adaptive affinitive patterns (RADAP) for facial expression recognition. The RADAP computes positional adaptive thresholds in the local neighbourhood and encodes multi‐distance magnitude features which are robust to intra‐class variations and irregular illumination variation in an image. Furthermore, they established cross‐distance co‐occurrence relations in RADAP by using logical operators. They proposed XRADAP, ARADAP, and DRADAP using xor, adder and decoder, respectively. The XRADAP engrains the quality of robustness to intra‐class variations in RADAP features using pairwise co‐occurrence. Similarly, ARADAP and DRADAP extract more stable and illumination invariant features and capture the minute expression features which are usually missed by regular descriptors. The performance of the proposed methods is evaluated by conducting experiments on nine benchmark datasets Cohn–Kanade+ (CK+), Japanese female facial expression (JAFFE), Multimedia Understanding Group (MUG), MMI, OULU‐CASIA, Indian spontaneous expression database, DISFA, AFEW and Combined (CK+, JAFFE, MUG, MMI & GEMEP‐FERA) database in both person dependent and person independent setup. The experimental results demonstrate the effectiveness of the proposed method over state‐of‐the‐art approaches.