![]() #Luxand blink pro windows 8 keygen![]() After liveness detection, an improved FaceNet will continue to recognize a face and provide the corresponding ID or UNKNOWN output for accurate identity authentication. The samples above are input into the convolutional neural network (CNN) for training to distinguish live faces and spoof attacks. IR images from live faces are used as positive samples, while IR images from photos or videos are used as negative samples. Therefore, this paper proposes a liveness detection approach based on infrared radiation (IR) images acquired using a Kinect camera. Common spoof attacks include photos, videos, masks, and replayed 3D face models. For instance, the researchers in inspected the threat of the online social network-based facial disclosure against that based on some commercial face authentication systems. A spoofing attack occurs when someone attempts to bypass a face biometric system by presenting a fake face in front of the camera. Most existing face recognition systems are vulnerable to spoofing attacks. The details will be introduced in Section 3.2.Īlthough the improved FaceNet framework can accurately recognize human faces, like other recognition systems, it cannot prevent cheating. In this study, for improving the application of the FaceNet model, we proposed two improved ways, namely, by improving the model and by building “unknown” data classification. In addition, end-to-end training of FaceNet simplifies the setup and shows that directly optimizing a loss relevant to the task at hand improves performance. Once this space has been produced, tasks such as face recognition can be easily implemented using standard techniques with FaceNet embeddings as feature vectors. It directly learns a mapping from face images in a compact Euclidean space where distances directly correspond to a measure of face similarity. FaceNet is a face recognition model with high accuracy, and it is robust to occlusion, blur, illumination, and steering. The accuracy of face recognition is greatly improved using the deep learning network because of its capability to extract the deep features of human faces. These techniques can generally be divided into two categories according to the face feature extracting methodology: methods that manually extract features on the basis of traditional machine learning and those that automatically acquire face features on the basis of deep learning. Therefore, researchers have developed several recognition techniques in the last decade. The reason is that this method is natural, nonintrusive, and low cost. Experimental results showed that the combination of the proposed liveness detection and improved face recognition had a good recognition effect and can be used for identity authentication.įace recognition is the most efficient and widely used among various biometric techniques, such as fingerprinting, iris scanning, and hand geometry. For improving the application of the authentication approach, we proposed two improved ways to run the FaceNet model. We combined the liveness detection and FaceNet model for identity authentication. FaceNet is a face recognition model, and it is robust to occlusion, blur, illumination, and steering. In comparison with other liveness detection cross-databases, our recognition accuracy was 99.8% and better than other algorithms. Accordingly, two types of IR images were learned through the deep network to realize the identification of whether images were from live individuals. Therefore, the IR pixels from live images have an evident hierarchical structure, while those from photos or videos have no evident hierarchical feature. IR images collected by the Kinect camera have depth information. Feature extraction and classification were carried out by a deep neural network to distinguish between real individuals and face spoofs. Face pictures were acquired by a Kinect camera and converted into IR images. The proposed liveness detection method based on infrared radiation (IR) images can deal with face spoofs. ![]() In this study, an advanced Kinect sensor was adopted to acquire infrared radiation (IR) images for liveness detection. ![]()
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