Acta Polytechnica Hungarica Vol. 10, No. 4, 2013 – 193 – Gender Classification using Multi-Level Wavelets on Real World Face Images Sajid Ali Khan, Muhammad Nazir, Naveed Riaz Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Plot # 67, Street # 9, H/8-4 Islamabad, 44000, Pakistan sajid.ali@szabist-isb.edu.pk, nazir@szabist-isb.edu.pk, n.r.ansari@szabist- isb.edu.pk Abstract: Gender classification is a major area of classification that has generated a lot of academic and research interest over the past decade or so. Being a recent area of interest in classification, there is still a lot of opportunity for further improvements in the existing techniques and their capabilities. In this paper, an attempt has been made to cover some of the limitations that the associated research community has faced by proposing a novel gender classification technique. In this technique, discrete wavelet transform has been used up to five levels for the purpose of feature extraction. To accommodate pose and expression variations, the energies of sub-bands are calculated and combined at the end. Only those features are used which are considered significant, and this significance is measured using Particle Swarm Optimization (PSO). The experimentation performed on real world images has shown a significant classification improvement and accuracy to the tune of 97%. The results also reveal the superiority of the proposed technique over others in its robustness, efficiency, illumination and pose change variation detection. Keywords: Gender Classification; Discrete Wavelet Transform; Particle Swarm Optimization; Feature Selection; Real World Face Images 1 Introduction In today’s technological world Gender Classification plays a vital role. It is widely used in applications such as customer-oriented advertising, visual surveillance, and intelligent user interfaces and demographics. With the evolution of human-computer interaction (HCI), in order to meet the growing demands for secure, reliable and convenient services, computer vision approaches like face identification, gesture recognition and gender classification will play an important role in our lives. Features are generally classified into two categories 1) Appearance-based (Global) and Geometric-based (Local) features. In the appearance-based feature extraction