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Facial Keypoint Detection Using Deep Learning and Computer Vision

机译:使用深度学习和计算机视觉的面部关键点检测

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With the advent of Computer Vision, research scientists across the world are working constantly working to expedite the advancement of Facial Landmarking system. It is a paramount step for various Facial processing operations. The applications range from facial recognition to Emotion recognition. These days, we have systems that identify people in images and tag them accordingly. There are mobile applications which identify the emotion of a person in an image and return the appropriate emoticon. The systems are put to use for applications ranging from personal security to national security. In this work, we have agglomerated computer vision techniques and Deep Learning algorithms to develop an end-to-end facial keypoint recognition system. Facial keypoints are discrete points around eyes, nose, mouth on any face. The implementation begins from Investigating OpenCV, pre-processing of images and Detection of faces. Further, a convolutional Neural network is trained for detecting eyes, nose and mouth. Finally, the CV pipeline is completed by the two parts mentioned above.
机译:随着计算机愿景的出现,世界各地的研究科学家正在不断努力加快面部地标系统的进步。这是各种面部处理操作的途径。应用范围从面部识别到情感识别。这些天,我们有系统,可以识别图像中的人员并相应地标记。有移动应用程序,其识别图像中的人的情绪并返回适当的表情符号。将系统用于从个人安全到国家安全的应用。在这项工作中,我们有凝聚的计算机视觉技术和深度学习算法来开发端到端的面部关键点识别系统。面部关键点是眼睛,鼻子,嘴巴的离散点,在任何脸上。该实现开始研究OpenCV,图像的预处理和面孔的检测。此外,训练卷积神经网络,用于检测眼睛,鼻子和嘴巴。最后,CV管道由上述两部分完成。

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