摘要：There are so many security devices such as code pin, dual control procedures, and ID card. However, those devices have the potential to be lost, stolen or duplicated by someone. Due to that reason, the authentication for driver by using their face is one of the potential solutions. In this proposed system, the real time person’s face is detected using a camera. The detected face is then processed inthe system which will recognize. The recognized face is then used as input to the Arduino, which connected to the automotive relay to active the engine’s starter vehicle. Viola-Jones method is applied as a method to detect and crop face area of the face image. The Canny edge method is applied as a method for segmenting the detected face. The Canny edge method will detect a wide range of edges from facealso reducing noise image. Fast Fourier Transform is applied as a feature extraction technique to extract the segmented face image. The extracted image is then used as input to the Artificial Neural Network (ANN) in order to recognize the face of the authorize person. Experimental results show the training and testing accuracy of 100 % and 100 %, respectively.
其他摘要：There are so many security devices such as code pin, dual control procedures, and ID card. However, those devices have the potential to be lost, stolen or duplicated by someone. Due to that reason, the authentication for driver by using their face is one of the potential solutions. In this proposed system, the real time person’s face is detected using a camera. The detected face is then processed inthe system which will recognize. The recognized face is then used as input to the Arduino, which connected to the automotive relay to active the engine’s starter vehicle. Viola-Jones method is applied as a method to detect and crop face area of the face image. The Canny edge method is applied as a method for segmenting the detected face. The Canny edge method will detect a wide range of edges from facealso reducing noise image. Fast Fourier Transform is applied as a feature extraction technique to extract the segmented face image. The extracted image is then used as input to the Artificial Neural Network (ANN) in order to recognize the face of the authorize person. Experimental results show the training and testing accuracy of 100 % and 100 %, respectively.
