A Unified Streaming Architecture for Real Time Face Detection and Gender Classification

Abstract

An integral part of interactive computing environments are systems that have the ability to process information about their users in real-time. In many cases it is desirable to not only recognize a human user but also to extract as much information about the user as possible, such as gender, ethnicity, age, etc. In this paper we present an FPGA implementation of a neural network configured specifically for performing face detection and gender classification in real-time video streams. Our streaming architecture performs the face and gender classification tasks at 30 frames per second on a small sized Virtex-4 FPGA, at accuracy comparable to that of a leading commercial software implementation.