A geometric and fractional entropy-based method for family photo classification. (September 2019)

• Record Type: Journal Article
• Authors: Kaljahi, Maryam Asadzadeh; Shivakumara, Palaiahnakote; Hu, Tianping; Jalab, Hamid A.; Ibrahim, Rabha W.; Blumenstein, Michael; Lu, Tong; Ayub, Mohamad Nizam Bin
• Is Part Of: Expert systems with applications. Volume 3(2019)
• Publication Date: 2019-09
• Subjects: Face recognition -- Facial points -- Facial geometric features -- Fractional entropy -- Convolutional neural networks -- Family photo classification; 006.33
• DOI: 10.1016/j.eswax.2019.100008
• Languages: English
• ISSNs: 2590-1885

Abstract

Highlights: We present a new idea for family and non-family photo classification. The proposed method explores the strengths of facial and the texture features. The texture feature are extracted by a new Fractional entropy based features. The proposed method combines geometric and entropy features in a new way. The CNN has been explored for final classification. Abstract: Due to the power and impact of social media, unsolved practical issues such as human trafficking, kinship recognition, and clustering family photos from large collections have recently received special attention from researchers. In this paper, we present a new idea for family and non-family photo classification. Unlike existing methods that explore face recognition and biometric features, the proposed method explores the strengths of facial geometric features and texture given by a new fractional-entropy approach for classification. The geometric features include spatial and angle information of facial key points, which give spatial and directional coherence. The texture features extract regular patterns in images. The proposed method then combines the above properties in a new way for classifying family and non-family photos with the help of Convolutional Neural Networks (CNNs). Experimental results on our own as well as benchmark datasets show that the proposed approach outperforms the state-of-the-art methods in terms of classification rate.