Revisiting Linear Discriminant Techniques in Gender Recognition

—Emerging applications of computer vision and pattern recognition in mobile devices and networked computing require the development of resourcelimited algorithms. Linear classification techniques have an important role to play in this context, given their simplicity and low computational requirements. The paper reviews the state-of-the-art in gender classification, giving special attention to linear techniques and their relations. It discusses why linear techniques are not achieving competitive results and shows how to obtain state-of-the-art performances. Our work confirms previous results reporting very close classification accuracies for Support Vector Machines (SVMs) and boosting algorithms on single-database experiments. We have proven that Linear Discriminant Analysis on a linearly selected set of features also achieves similar accuracies. We perform cross-database experiments and prove that single database experiments were optimistically biased. If enough training data and com...

• Type: Journal
• Year: 2011
• Where: PAMI
• Authors: Juan Bekios-Calfa, José Miguel Buenaposada, Luis Baumela
• Keywords: Computational Resources, Linear, Linear Classification Techniques, Operations Research, PAMI 2011