Measuring consumer interest by using local binary patterns

Using computers to analyze human faces has been an area of recent interest in computer science and psychology. It has been well researched that facial expressions do reflect cognitive behavior, and that individuals observe other's facial expressions and then use these to regulate their own behavior in social interactions. This paper describes an intelligent Approach for finding the costumer interest by analyzing the facial expressions of the customer and performs the required action. Upon entering the shop, a customer has his features scanned and analyzed by the computer and the customer is categorized as a browser, future customer, probable customer or buyer. This feature would also tell the sales personnel whether or not the customer requires or desires assistance in the first place. This, for the customer, can mean being directed to products they have been recognized to be more interested in, resulting in savings in time. In this paper we use Local Binary Patterns (LBP) for face recognition. LBP is a non-parametric kernel which summarizes the local spacial structure of an image and it is a invariant to monotonic gray-scale transformations; hence the LBP representation may be less sensitive to changes in illumination. This paper describes the theoretical and conceptual framework for such an intelligent sales assistant and discusses the technology used in its implementation.