TY - JFULL AU - M A Hannan and A. Hussain and S. A. Samad and K. A. Ishak and A. Mohamed PY - 2008/12/ TI - A Unified Robust Algorithm for Detection of Human and Non-human Object in Intelligent Safety Application T2 - International Journal of Computer and Information Engineering SP - 3837 EP - 3845 VL - 2 SN - 1307-6892 UR - https://publications.waset.org/pdf/10689 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 23, 2008 N2 - This paper presents a general trainable framework for fast and robust upright human face and non-human object detection and verification in static images. To enhance the performance of the detection process, the technique we develop is based on the combination of fast neural network (FNN) and classical neural network (CNN). In FNN, a useful correlation is exploited to sustain high level of detection accuracy between input image and the weight of the hidden neurons. This is to enable the use of Fourier transform that significantly speed up the time detection. The combination of CNN is responsible to verify the face region. A bootstrap algorithm is used to collect non human object, which adds the false detection to the training process of the human and non-human object. Experimental results on test images with both simple and complex background demonstrate that the proposed method has obtained high detection rate and low false positive rate in detecting both human face and non-human object. ER -