Face detection is one of the important topics in computer vision research and is the basis of many applications. A face detection algorithm based on improved Multi-Task Convolution Neural Network (MTCNN) is proposed in this paper. To increase the accuracy of eye location in complex situations, this method improves the network structure of MTCNN, builds a neural network model based on MTCNN using TensorFlow, and cascades an eye regression network. The Face-Net neural network model was used for training, and the obtained training model was used for detection. Experiments have shown that the accuracy on the LFW dataset is 0.9963 and the accuracy on the YouTube Faces DB dataset is 0.9512.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.