The continuous intensification of diversification and polytrope on market requirement has created demand for high flexibility and adaptability of production assembly. Human-computer interaction cooperation in mechanical assembly is an effective way to satisfy the market requirement. The organic texture character of hand skin image in mechanical assembly must be detected effectively. Such detection can in turn determine the capability of the robot to recognize and comprehend the action and behavior of the operator in human-computer interaction cooperation in mechanical assembly. A recognition algorithm is presented for hand skin recognition in image based on multi-color space information. The proposed algorithm is used in human-computer interaction coordination in mechanical assembly. A multi-component mixture model of skin color in the RGB color space is established for clustering by the off-line learning method of the Gaussian mixture model and expectation maximization algorithm. The hand skin information is expressed by mixture clustering. Furthermore

the law of skin color distribution in YCrCb color space is studied by on-line fast learning to sparse Gaussian model. The hand skin recognizer is designed based on muti-color space information. Image illuminance significantly influences the likelihood value of skin region model. The high threshold value is fitted for high image illuminance region

and low threshold value is fitted for low image illuminance region. Experimental results for hand skin recognition based on on-line sparse fast learning show that the redundance of likelihood in YCrCb three-color space channels is acceptable. The Gaussian mixture model and the corresponding learning method are valid. The defect of initial recognition can be revised by the YCrCb skin sparse Gaussian mixture model. Moreover

the on-line fast learning algorithm can enhance the adaptability of the skin model to the illumination distortion and improve the results of recognition in the fixed scene. The skin region is detected based on the results of different models in multi-color space. The time complexity of on-line learning algorithm is related to the integrity of initial skin region recognition

and the illumination equilibrium degree of scene is related to the parameter update quantity of recognition model. Both factors influence the space complexity of the recognition algorithm. Hand skin color modeling and region detection based on Gaussian model has good adaptability and practicability in mechnical assembly experiment. Hand skin detection based on the proposed algorithm obtains better recognition integrity than that based on ellipse clustering in YCrCb space. The convergence time of the recognition must be short to meet the real-time constraint of recognition.