Analysis of frontal face detection performance by using Artificial Neural Network (ANN) and Speed-Up Robust Features (SURF) technique
Nursabillilah Mohd Ali, Mohd Safirin Karis, Munawwarah Abd Aziz, Amar Faiz Zainal Abidin · AIP conference proceedings · 2016
The limitation of surveillance camera (CCTV) is related to the low resolution of the camera. In face detection, low resolution will affect the recognition rates and performance of the algorithm in terms of time response and its accuracy. This report leads to an Analysis of Frontal Face Detection by using Artificial Neural Network (ANN) and Speeded-Up Robust Feature (SURF) technique. The implementation of frontal face detection by using two varied techniques of image processing of Neural Network and SURF technique will be explored by using MATLAB software. Both techniques generate contrast of image performance in terms of time response and its accuracy. The expected output is to generate frontal face detection and to compare the performance of the image between the techniques selected. The comparison of performance in terms of time response, SURF is much better than ANN. This can be seen clearly by varying the resolution of the image. SURF keeps the fast record in identify the key feature in the image. In terms of accuracy, ANN system can be trained to discriminate face and non-face smoothly. Therefore, ANN is in advance to detect the face accurately while SURF has advantages in fast time response despite the resolution of the images.