Facial image analysis using local feature adaptation prior to learning
Many facial image analysis methods rely on learning-based techniques such as Adaboost or SVMs to project classifiers based on the selection of local image filters (e.g., Haar and Gabor filters) from large sets of training data. In general, the learning process consists of selecting discriminative image filters from a large feature pool that contains filters uniformly sampled from the parameter space. In this paper, we argue that we are able to improve these methods by incorporating a local feature adaptation technique prior to learning, which generates a more compact and meaningful pool of image filters, consequently reducing both learning and detection/recognition computational costs, while at the same time improving accuracies. In the first stage of our approach, local feature adaptation is carried out by a nonlinear optimization method that determines image filter parameters (such as position, orientation and scale) in order to match the geometrical structure of each training sample. In the second stage, Adaboost feature selection technique is applied to the adapted feature pool to obtain the final set of discriminative local image filters. We demonstrate the effectiveness and efficiency of the proposed framework in the face detection domain. In the experiments, we have applied our method using a pool of wavelet features, including Haar and Gabor filters. The results showed that with local feature adaptation, significant improvements in terms of detection accuracy and computational cost reduction are achieved over learning based on the same features sampled uniformly from the parameter space.