An Appearance-Based Gender Classification Using Radon Features Ratinder Kaur Sangha and Preeti Rai 1 Introduction Face is a characteristic feature of the human beings which contains identity, age, and emotions. Gender classification from a person’s face could play an important role in computer vision such as security surveillance systems, search engine, demographic studies, marketing research and performance enhancement (face recognition, smart human–computer interface). In real-world scenario, due to natural reasons, images might be occluded naturally like injury, wearing scarf, or sunglasses because of weather conditions [1] and thus, it becomes difficult to classify gender from such a partial occluded face. In this work, wavelet and Radon transforms are combined together for extracting features to classify male or female from facial information. The basic gender classification system used for our work is shown in Fig. 1 contains mainly three modules, i.e., preprocessing, features extraction, and classifier. In a pre-processing, basically relevant features are taken out, which are the most potential segment of an image. Feature extraction is done by combing wavelet and Radon transforms. The obtained features are then passed to a powerful supervised learning algorithm using SVM to discriminate male and female. This paper is assembled as follows: A review of the past research work in the area of gender classification is given in Sects. 2 and 3 describes our gender classification technique using wavelet and Radon transforms as features extraction and SVM as classifier. Experimental analysis and conclusion are shown in Sects. 4 and 5, respectively. R. K. Sangha (B ) · P. Rai Gyan Ganga Institute of Technology and Science, Jabalpur, India e-mail: ratinder18sangha@gmail.com © Springer Nature Singapore Pte Ltd. 2019 R. K. Shukla et al. (eds.), Data, Engineering and Applications, https://doi.org/10.1007/978-981-13-6347-4_15 159