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According to the requirements of tracking desired human for mobile robots, a human recognition approach which is based on the head-shoulder model is proposed. Firstly, the human head-shoulder models are extracted from the image obtained by human detection. Next, dimensionality reduction and weighted Hu moment invariants of the head-shoulder models are extracted as the feature vectors. Then, the head-shoulder models are identified as the front-back or profile models according to certain thresholds. Finally, the front-back or profile KNN classifier is used to determine which head-shoulder model belongs to the desired human, who needs to be tracked by a mobile robot. The experimental results show that the proposed approach has high recognition accuracy and is provided with real-time performance.