Tracking by Detection of Multiple Faces using SSD and CNN Features
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Multi-tracking of general objects and specific faces is an important topic in the field of computer vision applicableto many branches of industry such as biometrics, security, etc. The rapid development of deep neural networkshas resulted in a dramatic improvement in face recognition and object detection problems, which helps improve themultiple-face tracking techniques exploiting the tracking-by-detection method. Our proposed method uses facedetection trained with a head dataset to resolve the face deformation problem in the tracking process. Further, weuse robust face features extracted from the deep face recognition network to match the tracklets with trackingfaces using Hungarian matching method. We achieved promising results regarding the usage of deep face featuresand head detection in a face tracking benchmark.
Abstract
Multi-tracking of general objects and specific faces is an important topic in the field of computer vision applicableto many branches of industry such as biometrics, security, etc. The rapid development of deep neural networkshas resulted in a dramatic improvement in face recognition and object detection problems, which helps improve themultiple-face tracking techniques exploiting the tracking-by-detection method. Our proposed method uses facedetection trained with a head dataset to resolve the face deformation problem in the tracking process. Further, weuse robust face features extracted from the deep face recognition network to match the tracklets with trackingfaces using Hungarian matching method. We achieved promising results regarding the usage of deep face featuresand head detection in a face tracking benchmark.
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