Gender Classification through Fusion of Holistic and Region-based Facial Patterns

Year 2024, , 231 - 238, 28.03.2024

Maryam Eskandarı

https://doi.org/10.21605/cukurovaumfd.1460468

Abstract

In this study, a robust gender prediction system is proposed to fuse global and regional facial representations through score and feature level fusion. In order to extract facial features for gender classification, Binarized Statistical Image Features (BSIF) approach is applied on holistic and regional features of face images. The extracted features are then concatenated to combine the region-based information at feature level fusion. Then the optimized sub-set of features is selected using Particle Swarm Optimization (PSO) method. Finally, the holistic and regional features are combined at score level fusion to produce the final set of scores for gender classification. This study applies Weighted Sum (WS) rule strategy for score level fusion. The experimental results are performed on Multiple Biometric Grand Challenge (MBGC) and CASIA-Iris-Distance databases with consideration of subject-disjoint training and testing evaluation to testify the validity of the proposed gender classification system. The experimental results of the study demonstrate the success of the proposed scheme for gender prediction.

Keywords

Gender classification, Region-based BSIF, Information fusion, Optimized feature selection

Bütünsel ve Bölge Bazlı Yüz Kalıplarının Birleştirilmesi Yoluyla Cinsiyet Sınıflandırması

Abstract

Bu çalışmada, evrensel ve bölgesel yüz görünüşlerini skor seviyesi birleştirme ve öznitelik seviyesi birleştirme yoluyla bir araya getirmek için güçlü bir cinsiyet tahmin sistemi önerilmektedir. Cinsiyet sınıflandırmasında yüz özniteliklerini çıkarmak için, İkili İstatistiksel Görüntü Öznitelikleri (BSIF) yaklaşımı yüz görüntülerine bütünsel ve bölgesel olarak uygulanmıştır. Ardından çıkarılan öznitelikler bölgesel bilgilerin öznitelik seviyesi birleştirmesi düzeyinde bir araya getirilir, Optimize edilmiş öznitelik alt kümesi, Parçacık Sürü Optimizasyonu (PSO) yöntemi kullanılarak seçilir. Son olarak, bütünsel ve bölgesel bilgiler, cinsiyet sınıflandırması için nihai skorları üretmek amacıyla skor seviyesi birleştirme seviyesinde bir araya getirilir. Bu çalışma, skor seviyesi birleştirme için Ağırlıklı Toplam (WS) kuralı stratejisini kullanmaktadır. Deneysel sonuçlar, önerilen cinsiyet sınıflandırma sisteminin geçerliliğini test etmek amacıyla Multiple Biometric Grand Challenge (MBGC) ve CASIA-Iris-Distance veritabanlarında, özne-ayrık eğitim ve test değerlendirmesi dikkate alınarak gerçekleştirilmiştir. Çalışmanın deneysel sonuçları cinsiyet tahmin sisteminin başarılı olduğunu göstermiştir.

Keywords

Cinsiyet sınıflandırması, Bölgesel BSIF, Bilgi kaynaşımı, Optimize edilmiş öznitelik seçimi

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