In this paper a new texture descriptor is presented for biometric authentication systems based on hand dorsal texture characteristics. First, different hand images are collected using an acquisition device, then the images are mapped from RGB color space to YUV and the five fingers are isolated from images with thresholding of the U component for skin detection. Utilizing the center of gravity, length and direction of the fingers, the finger knuckle print and nail regions are determined. Then, the texture features of finger knuckle are described using four directional patterns based on statistical thresholding. Moreover, the features of nails are described using discrete wavelet transform. In addition, utilizing a cost function in the evolutionary particle swarm optimization algorithm, a set of significance coefficients is applied to the textural features of the hand, and as a result, these features are optimized. Finally, using the d1 distance measurement criterion, the similarity of the images has been calculated and the identity of individuals has been identified with the recognition rate of 92.13%. The experimental results on a set of images collected by the authors, called FKP_Nail, show that the proposed method is more accurate and faster than known existing methods while it uses simpler calculations.