Dynamic attention priors: a new and efficient concept for improving object detection

Recent psychophysical evidence in humans suggests that visual attention is a highly dynamic and predictive process involving precise models of object trajectories. We present a proof-of-concept that such predictive spatial attention can benefit a technical system solving a challenging visual object detection task. To this end, we introduce a Bayes-like integration of the so-called dynamic attention priors (DAPs) and dense detection likelihoods, which get enhanced at predicted object positions obtained by the extrapolation of trajectories. Using annotated video sequences of pedestrians in a parking lot setting, we quantitatively show that DAPs can improve detection performance significantly as compared to a baseline condition relying purely on pattern analysis

Face and Facial Feature Localization

In this paper we present a general technique for face and facial feature localization in 2D color images with arbitrary background. In a previous work we studied an eye localization module, while here we focus on mouth localization. Given in input an image that depicts a sole person, first we exploit the color information to limit the search area to candidate mouth regions, then we determine the exact mouth position by means of a SVM trained for the purpose. This component-based approach achieves the localization of both the faces and the corresponding facial features, being robust to partial occlusions, pose, scale and illumination variations. We report the results of the separate modules of the single feature classifiers and their combination on images of several public databases

Fast Face Detection Using a Cascade of Neural Network Ensembles

We propose a (neat) real-time face del.ect.or using a cascade of parallel neuTal net.work: (NN) ensembles for enhanced detection aCCUTacy and efficiency. First, we fon7/. a coordinated NN ensemble by sequentially tmining a set of neuml netw01ks with the same topology. The I.mining implicitly paTtitions the face space into a nurnbeT of di.~joint n:gions, and each NN 'is specialized in a spec~fic S11,b-l·egion. Second, to reduce the /.otal comp'lttal.ion cost for t.he face det.ect.ion, a series of NN ensembles aTe cascaded s1tch thai. the complexity of base nel:w01'ks increases. Our pTOJlosed apJlTOach achieves 1/,P to 94 % detection Tate (CM U + MIT test set) and 3-4 % frames/sec. det.ect.ion speed on a nan7/.al PC (P-IV, 3.0GHz)

Component-based Face Recognition with 3D Morphable Models

We present a system for pose and illumination invariant face recognition that combines two recent advances in the computer vision field: 3D morphable models and componentbased recognition. A 3D morphable model is used to compute 3D face models from three input images of each subject in the training database. The 3D models are rendered under varying pose and illumination conditions to build a large set of synthetic images. These images are then used for training a component-based face recognition system. The face recognition module is preceded by a fast hierarchical face detector resulting in a system that can detect and identify faces in video images at about 4 Hz. The system achieved a recognition rate of 88% on a database of 2000 real images of ten people, which is significantly better than a comparable global face recognition system. The results clearly show the potential of the combination of morphable models and component-based recognition towards pose and illumination invariant face recognition