Abstract: In man-machine coordinated assembly, the precise finger image feature should be extracted and recognized to accurately present the hand posture. In order to enrich the hand information of co-operator, a multi-classification algorithm based on Laplace approximation Gaussian process is put forward to achieve the knuckle recognition based on the hand image. According to the assumption that the category information is unrelated to each other, the learning of knuckle image mid-level offset measure features is transformed into the learning of random quantities. A multi-class Gaussian process learning algorithm based on Laplace approximation Gaussian process is presented by analyzing the posterior computation in binary multi-class Gaussian field and constructing the positive definite kernel function of knuckle image middle-level random information. The knuckle image training and learning and the image recognition of fixed threshold value are conducted using the mid-level distribution learning and predicting algorithm. The recognition results show that this method is feasible.

Key words:  Gaussian process, image recognition, knuckle image, feature learning