The traditional sparse coding model has poor feature selection performance,and the negative coefficient in the sparse-coefficient vectors may cause high dimensions and information redundancy,which is harmful for the object recognition.To solve the problem,a sparse coding model based on the adaptive elastic net is proposed.The model firstly extracts the feature points by AGAST (Adaptive and Generic Corner Detection Based on the Accelerated Segment Test) detector in scale space,and describes them by FREAK algorithm.Then the sparse-coefficient vectors are calculated out by applying an adaptive elastic net regression model that can select strong correlation features.Finally,target recognition is realized by classifier.The result shows that feature detection algorithm is more robust to the changes of scale,viewpoint,brightness,and rotation,and the recognition performance of the model has great improvement under the restriction of adaptive elastic net model.