Customer and target individual face analysis for retail analytics

Statistics about customer satisfaction are extremely valuable for retail stores. We present an automated approach to obtaining this information using image processing and deep learning. Our system combines face detection and tracking, best view estimation, repeat customer identification, blacklisted customer warnings, and facial sentiment classification. A series of experiments shows that each of the modules in the combined systems achieves satisfactory results.