The age verification is an important task in various context of applications like access control in spaces in hotels which are prohibited for children and teenagers, in dangerous spaces for children and in public area during a spread of virus among others. In fact, the age verification consists in classifying the face images into different age groups while dealing with the face appearance variation affected by occlusion, pose variation, low resolution, scale variation and illumination variation. This work introduced an access control application based on the age verification in an uncontrolled environment. In fact, we proposed a new two-level age classification method based on deep learning in order to classify the face images into eight age groups. Actually, the two-level classification strategy help reducing the confusion between the inter and intra age groups. Our experiments were performed on the multi-constrained Adience benchmark. The obtained results illustrate the effectiveness and robustness of the proposed age classification method in an uncontrolled environment.

Intelligent video surveillance systems represent a potent tool for preserving human security in public places. Indeed, these surveillance systems are requested in several real-life scenarios, in order to assist security guards by alerting them in abnormal situations and helping them to retrieve a suspicious person. Especially, intelligent video surveillance systems based on UAV-sensors have the asset of monitoring large as well as difficult access spaces. In this scope, we introduce a new approach for suspicious person retrieval from UAV-sensors. The proposed approach implies two complementary phases which are an offline phase and an inference phase. Within these phases, a scene stabilization step is carried out. The offline phase allows building the non-person/person model as well as the person retrieval model. Nonetheless, the inference phase enables to detect persons and retrieve suspicious ones using the already generated models. The main contribution of the proposed approach is the use of part-level deep features in order to retrieve persons. The experimental results validate the contributions of our approach compared to the state-of-the-art approaches.

We introduce a new computer vision based system for robust traffic sign recognition and tracking. Such a system presents a vital support for driver assistance in an intelligent automotive. Firstly, a color based segmentation method is applied to generate traffic sign candidate regions. Secondly, the HoG features are extracted to encode the detected traffic signs and then generating the feature vector. This vector is used as an input to an SVM classifier to identify the traffic sign class. Finally, a tracking method based on optical flow is performed to ensure a continuous capture of the recognized traffic sign while accelerating the execution time. Our method affords high precision rates under different challenging conditions.

Accidents caused by reduced concentration of drivers on traffic signs indications continue to represent an important part of accident-prone situations. Face to this threat, our work aims to develop a vision-based traffic sign recognition method based on a two-step recognition and 3D distance computing module. Firstly, a monocular color based segmentation method is applied to generate traffic sign candidates. Then, HoG features are applied to encode the detected traffic signs and compute the feature vector. This vector is used as an input to a SVM classifier to identify the traffic sign class. Secondly, a dense disparity map between the left and right images is created for the recognized traffic sign region to compute its distance to the vehicle carrying the stereovision. Our method affords high precision rates under different weather conditions. Moreover, it operates with a timing that is reasonable for real-time applications. The obtained results, compared to leading methods from the literature, prove the efficiency of our proposed method.

Over the last two decades, the advances in computer vision and pattern recognition power have opened the door to new opportunity of automatic facial expression recognition system. In this work, we have introduced a new feature-based approach for facial expressions recognition. The proposed approach provides full automatic solution to identify human expressions as well as overcoming facial expressions variation and intensity problems. Facial features component were automatically detected and segmented. Then, we have detected facial feature points which go with facial expression deformations. Afterwards, distances between these points were computed and used through Data mining technique to generate a set of relevant prediction rules able to classify facial expressions. We took into account the intensity of JOY expression. Thus, we have defined SMILE expression as the lowest intensity of JOY. Seven facial expression classes were defined: JOY, SMILE, SURPRISE, DISGUST, ANGER, SADNESS, and FEAR We have appraised experimental study to evaluate the performance of the proposed solution.

This paper introduces a novel low-computation discriminative regions representation for expression analysis task. The proposed approach relies on interesting studies in psychology which show that most of the descriptive and responsible regions for facial expression are located around some face parts. The contributions of this work lie in the proposition of new approach which supports automatic facial expression recognition based on automatic regions selection. The regions selection step aims to select the descriptive regions responsible or facial expression and was performed using Mutual Information (MI) technique. For facial feature extraction, we have applied Local Binary Patterns Pattern (LBP) on Gradient image to encode salient micro-patterns of facial expressions. Experimental studies have shown that using discriminative regions provide better results than using the whole face regions whilst reducing features vector dimension.

In this paper we deal with the problem of lowering down the difficulty of face detection in video. Most of the recently developed systems swap detection accuracy for higher speeds, or vice versa. We have proposed a robust approach which makes use of spatial and temporal information in video to reduce time execution and improve precision rate. Our experiments show that our proposed approach proves efficiency without sacrificing real-time performance which makes it well-suited for live video applications.