A Deep Learning-Based Framework for Accurate Facial Ethnicity Classification and Efficient Query Retrieval
Geraldine Bessie Amali, K. S. V. Keerthana, Jaiesh Sunil Pahlajani · Advances in computational intelligence and robotics book series · 2020
Facial images carry important demographic information such as ethnicity and gender. Ethnicity is an essential part of human identity and serves as a useful identifier for numerous applications ranging from biometric recognition, targeted advertising to social media profiling. Recent years have seen a huge spike in the use of convolutional neural networks (CNNs) for various visual, face recognition problems. The ability of the CNN to take advantage of the hierarchical pattern in data makes it a suitable model for facial ethnicity classification. As facial datasets lack ethnicity information it becomes extremely difficult to classify images. In this chapter a deep learning framework is proposed that classifies the individual into their respective ethnicities which are Asian, African, Latino, and White. The performances of various deep learning techniques are documented and compared for accuracy of classification. Also, a simple efficient face retrieval model is built which retrieves similar faces. The aim of this model is to reduce the search time by 1/3 of the original retrieval model.