According to the problem of excessive energy consumption caused by the unreasonable distribution of cluster head nodes in the large-scale Wireless Sensor Network (WSN), a Hierarchical Routing Protocol for wireless sensor networks based on Non-uniform Clustering (HRPNC) was designed. HRPNC combined the idea of clustering in Low Energy Adaptive Clustering Hierarchy (LEACH), and basing on stratification improved the algorithms of competitive radius regarding Energy-Balanced Unequal Clustering routing protocol for WSN (DEBUC). Through taking advantage of hierarchical mechanism and the mechanism of competition, the distribution of the cluster heads turned out to be more reasonable and the energy consumption of such nodes got balance effectively. In the simulation performed on the Matlab, the life cycle of HRPNC was higher than that of the LEACH and DEBUC by about 500 and 300 rounds respectively. The average residual energy of the nodes with HRPNC was higher than that of the nodes with LEACH and DEBUC. As to the energy consumption, it remained lower and more stable during the survival phase. Besides, compared with LEACH and DEBUC, the aggregate of data packet of HRPNC was 300% and 130% higher respectively. What is more, under different simulations, the packet loss rate of HRPNC was lower than that of LEACH and DEBUC. The experimental results show that HRPNC can not only extend the lifetime of the network, and increase network stability and the number of data transmission, but also reduce the loss rate of data transmission effectively.

According to the fact that the performance of skin-color detection is greatly affected by the illumination, a kind of skin-color detection algorithm with good stability was proposed. According to the characteristic of face symmetry, the pixel correction algorithm was used to replace too bright or too dark pixels on the face area with normal ones, and then an adaptive method was used for skin-color detection, in which the corresponding chroma threshold was determined dynamically by the brightness of pixel. The experimental results show that, compared to other algorithms such as the YCbCr single Gauss model for skin-color detection, more than 10% of positive detection rate was increased and the false positive rate was reduced by 5% with the proposed algorithm under different light intensity. Moreover, the stability of the proposed algorithm is significantly enhanced.