In this paper, we propose a new human detection descriptor based on a combination of three major types of visual information: color, shape, and texture. Shape features are extracted based on both the gradient concept and the phase congruency in LUV color space. The Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The fusing of these complementary information yields to capture a broad range of the human appearance details that improves the detection accuracy. The proposed features are formed by computing the phase congruency of the three color channels in addition to the gradient magnitude and CSLBP value for each pixel in the image with respect to its neighborhood. Only the maximum phase congruency values are selected from the corresponding color channels. The histogram of oriented phase and gradients, as well as the histogram of CSLBP values for the local regions of the image, are determined. These histograms are concatenated to construct the proposed descriptor, that fuses the shape and texture features, and it is named as Chromatic domain Phase features with Gradient and Texture (CPGT). Several experiments were conducted to evaluate the performance of the proposed CPGT descriptor. The experimental results show that the proposed descriptor has better detection performance and lower error rates when compared to several state of art feature extraction methodologies.