CSEIT20613 | Accepted : 05 Jan 2020 | Published : 15 Jan 2020 | January-February-2020 [ 6 (1) : 20-25 ] International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2020 IJSRCSEIT | Volume 6 | Issue 1 | ISSN : 2456-3307 DOI : https://doi.org/10.32628/CSEIT20613 20 A Survey Paper on Gender Classification using Deep Learning Brijesh Patel 1 , Dr. Sheshang Degadawala 2 1 PG Scholar, Computer Engineering, Sigma Institute of Engineering, Vadodara, Gujarat, India 2 Head of the Department, Computer Engineering, Sigma Institute of Engineering, Vadodara, Gujarat, India ABSTRACT With technological advancements many small to large, simple to complex activities are automated. Growth of Artificial Intelligent techniques has eased the way we would look to solve the real-world problems. One such area which has recently gained lot of attention is the biometric analytics like Face Recognition, Fingerprint, voice etc. It involves extracting features such as face expressions, gender, age etc. Gender information plays a vital role in areas such as human computer interaction, crime detection, gender preferences, facial biometrics for digital payments etc. This paper proposes gender recognition using facial images and fingerprints with different algorithm like Visual Geometry Group-VGGNet16‖, Segmentation based Fractal Texture Analysis (SFTA) etc. Keyword: - Gender Identification, Biometrics, Face Image, Fingerprint, SFTA, SVM, CNN. I. INTRODUCTION Identifying human’s gender based upon their biometric traits, such as fingerprint, palm print, face, gait, iris and voice plays a vital role in forensics application and has now become an important area of research in biometrics. Now-a-days e-security is in acute need of finding accurate, secure and cost- effective alternative to password and personal identification. As the saying goes ―Face is the index of mind‖, human face can depict many characteristics such as ethnicity, gender, age, emotions etc. Facial analysis has recently gained lot of attention from research fraternity. Undoubtedly fingerprint biometrics is one of the most reliable and viable solution for all these problems. A person’s fingerprint data is distinctive and cannot be relocated. A successful gender classification approach can boost the performance of many other applications including face recognition and smart human-computer interface. Face recognition, expression identification, age determination, racial binding and gender classification are common examples of image processing computerization. Gender classification is very straightforward for us like we can tell by the person’s hair, nose, eyes, mouth and skin whether that person is a male or female with a relatively high degree of confidence and accuracy; however, can we program a computer to perform just as well at gender classification? The conventional sequence for recent real-time facial image processing consists of five steps: face detection, noise removal, face alignment, feature representation and classification. With the aim of human gender classification, face alignment and feature vector extraction stages have been re- examined keeping in view the application of the system on smartphones.