Gender Prediction Based on Morphometry of Eyes Using Deep Learning Models

Abstract

In the modern days, the growth of online social networking websites and social media leads to an increasing adoption of computer-aided image recognition systems that automatically recognize and classify human subjects. One such familiar one is the anthropometric analysis of the human face that performs craniofacial plastic and reconstructive surgeries. To analyze the impact on facial anthropometrics, it is also essential to consider various factors such as age, gender, ethnicity, socioeconomic status, environment, and region. The repair and reconstruction of facial deformities to find the anatomical dimensions of the facial structures as used by plastic surgeons for their surgeries result from the physical or facial appearance of an individual. Gender classification plays an important role of identifying the person as either male or female using biometric images. The main goal is to interact with the system, so that gender differences are produced effectively and accurately. Hence, it is essential to select the features optimally to achieve better accuracy. Data mining or machine learning techniques can be useful to infer properties such as the gender or age of the people involved to analyze the human activities. Towards this end, the proposed work focuses on gender recognition thereby building a model to scan the eye image of a patient and determine if the gender of the patient is either male or female by applying deep learning methods. It is identified from the work that deep learning network yields a better performance for gender based classification based on the morphometry of eyes.