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Abstract
Facial recognition systems were first explored in the security system to identify and compare with other biometrics, iris recognition etc from the database. Recently it has been added in many fields of study and has become a commercial form of identification and marketing tool. This paper describes the study of different types of algorithms being commonly used for face recognition. Our study will help in the retention of customers who adds customer value and thereby increasing the customer lifetime value. The facial recognition system could be set up at the billing desk. The faces of the customers would be stored as image automatically in a database along with their purchase history, which is then matched with any incoming customer. Accordingly, the frequency of each customer would be stored in the database & loyalty discounts could be provided to them on the basis of their purchase frequency. This data could also be used to predict the sales, market basket analysis and other marketing analytics applications for the retail store. We will use the algorithm that yields better accuracy in order to detect potential customers purchasing in a retail store.
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Abstract
Facial recognition systems were first explored in the security system to identify and compare with other biometrics, iris recognition etc from the database. Recently it has been added in many fields of study and has become a commercial form of identification and marketing tool. This paper describes the study of different types of algorithms being commonly used for face recognition. Our study will help in the retention of customers who adds customer value and thereby increasing the customer lifetime value. The facial recognition system could be set up at the billing desk. The faces of the customers would be stored as image automatically in a database along with their purchase history, which is then matched with any incoming customer. Accordingly, the frequency of each customer would be stored in the database & loyalty discounts could be provided to them on the basis of their purchase frequency. This data could also be used to predict the sales, market basket analysis and other marketing analytics applications for the retail store. We will use the algorithm that yields better accuracy in order to detect potential customers purchasing in a retail store.