Product Review Based on Optimized Facial Expression Detection
This work offers an incremental improvement in facial expression detection for product review applications in retail settings.
The paper proposes a method for product review based on facial expression detection using a modified Harris algorithm that reduces time complexity for feature extraction, achieving faster performance with nearly equal accuracy.
This paper proposes a method to review public acceptance of products based on their brand by analyzing the facial expression of the customer intending to buy the product from a supermarket or hypermarket. In such cases, facial expression recognition plays a significant role in product review. Here, facial expression detection is performed by extracting feature points using a modified Harris algorithm. The modified Harris algorithm reduced the time complexity of the existing feature extraction Harris Algorithm. A comparison of time complexities of existing algorithms is done with proposed algorithm. The algorithm proved to be significantly faster and nearly accurate for the needed application by reducing the time complexity for corner points detection.