This paper presents a new pedestrian detection descriptor named Histogram of Oriented Phase and Gradient (HOPG) based on a combination of the Histogram of Oriented Phase (HOP) features and the Histogram of Oriented Gradient features (HOG). The proposed descriptor extracts the image information using both the gradient and phase congruency concepts. Although the HOG based method has been widely used in the human detection systems, it lacks to deal effectively with the images impacted by the illumination variations and cluttered background. By fusing HOP and HOG features, more structural information can be identified and localized in order to obtain more robust and less sensitive descriptors to lighting variations. The phase congruency information and the gradient of each pixel in the image are extracted with respect to its neighborhood. Histograms of the phase congruency and the gradients of the local segments in the image are computed with respect to its orientations. These histograms are concatenated to construct the HOPG descriptor. The performance evaluation of the proposed descriptor was performed using INRIA and DaimlerChrysler datasets. A linear support vector machine (SVM) classifier is used to train the pedestrians. The experimental results show that the human detection system based on the proposed features has less error rates and better detection performance over a set of state of the art feature extraction methodologies.