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[Software Engineering] Combining-face-detection-and-people-tracking-in-v DL : 0: Face detection algorithms are widely used in computer vision as they provide fast and reliable results depending on the application domain. A multi view approach is here presented to detect frontal and profile pose of people face using Histogram of Oriented Gradients, i.e. HOG, features. A K-mean clustering technique is used in a cascade of HOG feature classifiers to detect faces. The evaluation of the algorithm shows similar performance in terms of detection rate as state of the art algorithms. Moreover, unlike state of the art algorithms,our system can be quickly trained before detection is possible. Performance is considerably increased in terms of lower computational cost and lower false detection rate when combined with motion constraint given by moving objects in video sequences. The detected HOG features are integrated within a tracking framework and allow reliable face tracking results in several tested surveillance video sequences.
Date : 2025-12-25 Size : 287kb User : linuszhao
[Software Engineering] facePeyes_detection DL : 0: 非常好用的人脸检测器，针对正面人脸检测和眼睛区域检测。检测效率高，检测时间在200ms左右。-Very easy to use face detector for frontal face detection and eye area detection. High detection efficiency, detection time is about 200ms.
Date : 2025-12-25 Size : 13.14mb User : 梁肖
[Software Engineering] Facial_Feature_Tracking DL : 0: 通过建议一个人脸形状先验模型关注该问题，该模型基于受限Boltzmann Machines (RBM)及其变种构建。特别的，我们首先基于深度信任网络构建一个模型以获取接近正视角的表情变化的人脸形状变量。为了解决姿态变化问题，我们将正面人脸形状先验模型整合到一个3路（3-way）RBM模型，其可以获取正面人脸形状和非正面人脸形状间的关系。最后，我们建议一个方法，将人脸先验模型和人脸特征点的图像度量系统性地组合在一起。-we address this problem by proposing a face shape prior model that is constructed based on the Restricted Boltzmann Machines (RBM) and their variants. Specifically, we first construct a model based on Deep Belief Networks to capture the face shape variations due to varying facial expressions for near-frontal view. To handle pose variations, the frontal face shape prior model is incorporated into a 3-way RBM model that could capture the relationship between frontal face shapes and non-frontal face shapes. Finally, we introduce methods to systematically combine the face shape prior models with image measurements of facial feature points. Experiments on benchmark s show that with the proposed method, facial feature points can be tracked robustly and accurately even if faces have significant facial expressions and poses.
Date : 2025-12-25 Size : 1.31mb User : 郭继东