Multi-view Face Detection Using Deep Convolutional Neural Networks

In this paper we consider the problem of multi-view face detection. While there has been significant research on this problem, current state-of-the-art approaches for this task require annotation of facial landmarks, e.g. TSM [25], or annotation of face poses [28, 22]. They also require training dozens of models to fully capture faces in all orientations, e.g. 22 models in HeadHunter method [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method that does not require pose/landmark annotation and is able to detect faces in a wide range of orientations using a single model based on deep convolutional neural networks. The proposed method has minimal complexity; unlike other recent deep learning object detection methods [9], it does not require additional components such as segmentation, bounding-box regression, or SVM classifiers. Furthermore, we analyzed scores of the proposed face detector for faces in different orientations and found that 1) the proposed method is able to detect faces from different angles and can handle occlusion to some extent, 2) there seems to be a correlation between dis- tribution of positive examples in the training set and scores of the proposed face detector. The latter suggests that the proposed methods performance can be further improved by using better sampling strategies and more sophisticated data augmentation techniques. Evaluations on popular face detection benchmark datasets show that our single-model face detector algorithm has similar or better performance compared to the previous methods, which are more complex and require annotations of either different poses or facial landmarks.Comment: in International Conference on Multimedia Retrieval 2015 (ICMR

Methods of Automatic Face Angle Recognition for Life Support and Safety Systems

The vision of the surrounding and people that are within eyeshot influences the human well-being and safety. The rationale of system development that allows recognizing faces from difficult perspectives online and informing timely about approaching people is undisputed. The manuscript describes the methods of automatic detection of equilibrium face points in the bitmap image and methods of forming 3D face model. The optimal search algorithm for equilibrium points has been chosen. The method of forming 3D face model basing on a single bitmap image and building up the face image rotated to the preset angle has been proposed. The algorithm for estimating the angle and algorithm of the face image rotation have been implemented. The manuscript also reviews the existing methods of forming 3D face model. The algorithm for the formation of 3D face model from a single bitmap image and a set of individual 3D models have been proposed as well as the algorithm for forming different face angles with the calculated 3D face model aimed to create biometric vectors cluster. Operation results of the algorithm for face images formation from different angles have been presented

Methods of Automatic Face Angle Recognition for Life Support and Safety Systems

The vision of the surrounding and people that are within eyeshot influences the human well-being and safety. The rationale of system development that allows recognizing faces from difficult perspectives online and informing timely about approaching people is undisputed. The manuscript describes the methods of automatic detection of equilibrium face points in the bitmap image and methods of forming 3D face model. The optimal search algorithm for equilibrium points has been chosen. The method of forming 3D face model basing on a single bitmap image and building up the face image rotated to the preset angle has been proposed. The algorithm for estimating the angle and algorithm of the face image rotation have been implemented. The manuscript also reviews the existing methods of forming 3D face model. The algorithm for the formation of 3D face model from a single bitmap image and a set of individual 3D models have been proposed as well as the algorithm for forming different face angles with the calculated 3D face model aimed to create biometric vectors cluster. Operation results of the algorithm for face images formation from different angles have been presented

High-Speed Automatic Human Face Recognition System

The purpose of this project is to help develop further techniques and uses of high speed automatic facial recognition. This technology is used to detect requested people, such as criminals and missing people. Our focus is face feature extraction which is broken down into three stages. The first stage is face detection which may be performed with issues under various environments such as different sizes of the input faces, difficult lighting conditions, and multiple camera angles. To tackle these issues, we found solutions as following: for different sizes of the input faces, we recorded training images with different sizes by adjusting the distance from the recorded face to the input camera; for lighting issues, we changed the lighting of the environment that the subject was in by overexposure and underexposure the image; for camera angles, we trained the system with a large amount of images related with assorted angles of the camera. Higher angles were focused on to simulate a surveillance camera in an environment like a store or shopping center. A combination of the issues tested the outer functional limits of the program. Tests were first conducted on the team members to understand the functionality of the program. After, more individuals were added to the database. We created design criteria and wanted the speed, reliability, its security, and its legality to be the most important aspect of the program. Another main feature of the program is to extract multiple images of the human face and place the images in a database created specifically for each person. The concluding feature of the program is to compare the images of the human subject against individuals already registered in the database to quickly and accurately identify the person. The team is continuing to research the most efficient way to implement this technology.https://ecommons.udayton.edu/stander_posters/2019/thumbnail.jp