Face representation tends to have a drastic effect on face recognition systems since input images can be greatly affected by multiple environmental factors such as pose, expression, light intensity, backgrounds, and image resolutions which contribute to reducing the similarity of face samples belonging to the same individual. These representations used in detection systems are termed as descriptors. These descriptors are fundamentally vectors consisting of quantized facial features, allowing convenient storage of facial information along with the ability of determining similarities. The ability for systems to successfully detect and recognize human faces has had vast applications in various fields of security, surveillance, banking, and retail etc. over the past years and continues to rapidly expand. Recently, this technology has found its way into the retail market where it is being used to extract valuable insights pertaining to different retail sections such as product tracking, customer behavior detection, customer location tracking, staff monitoring as well as surveillance. Inferences from such analytics play a vital role in making paramount decisions and predictions affecting business performance.

Building up to the above-mentioned practical implementations of face recognition in retail, we observe the majority of the practices to be computationally extensive and require expensive hardware. We, therefore, propose a more active analysis of the customer behavior information through the use of video analytics, and image processing. We present an automated web-based approach to study and display customer behavior through in-store analytics. This system combines face detection and recognition, repeat customer identification, and customer purchasing behavior analysis. Moreover, we propose using an advanced React-based POS dashboard to be integrated with our face recognition model and log customer purchases alongside, thus allowing retail owners to manage their sales analytics as an added feature as well.