We present a novel face detection approach based on a convolutional neural architecture, designed to de tec t and precisely localize highly variable face pa t te rns , i n complex real world images. Our system automatically synthesizes s imple problem-specific feature ext rac tors f rom a training set of face and non face patterns, without making any assumptions or using any hand-made design co...

In this paper, we propose a system that takes the attendance of students for classroom lecture. Our system takes the attendance automatically using face recognition. However, it is difficult to estimate the attendance precisely using each result of face recognition independently because the face detection rate is not sufficiently high. In this paper, we propose a method for estimating the atten...

We present a robust and portable visual-based skin and face detection system developed for use in a multiple speaker teleconferencing system, employing both audio and video cues. An omni-directional video sensor is used to provide a view of the entire visual hemisphere, thereby allowing for multiple dynamic views of all the participants. Regions of skin are detected using simple statistical met...

In this paper, we propose a heterogeneous committee machine for face processing including face detection and recognition. Our proposed system consists of two components, Face Detection Committee Machine (FDCM) and Face Recognition Committee Machine (FRCM), which employs three and five well-known state-of-the-art approaches respectively. We engage different methodologies to solve the face detect...

Recently significant performance improvement in face detection was made possible by deeply trained convolutional networks. In this report, a novel approach for training state-of-the-art face detector is described. The key is to exploit the idea of hard negative mining and iteratively update the Faster R-CNN based face detector with the hard negatives harvested from a large set of background exa...

In this paper we present a visual learning approach that uses non-parametric probability estimators. We use entropy analysis over the training set in order to select the features that best represent the pattern class of faces, and set up discrete probability models. These models are tested in the context of maximum likelihooddetection of faces. Excellent results are reported in terms of the cor...

In this paper we propose a new approach to automatic human face detection which employs anisotropic Gaussian filters as local image descriptors. We then show how the paradigm of classifier combination can be used for building a face detector that outperforms the current state–of– the–art systems, while remaining fast enough for being used in real–time systems. We report a number of results on s...

We describe a face detection algorithm, which characterizes and localizes skin regions and eyes in 2D images using color information and Support Vector Machine. The method is scale-independent, works on images of either frontal, rotated faces, with a single person or group of people, and does not require any manual setting or operator intervention. The algorithm can be used in face image databa...

In this paper, a new fast method for detecting human faces in color images using wavelet transform is proposed. The face detection algorithm has three stages, where chrominance, shape and frequency information are used respectively. The algorithm starts at the lower resolution version of the image obtained from the wavelet transform, so that the amount of data to be processed is greatly reduced...

Tliis paper presents the method to determine the order of feature (attention) points for face detection. The order of feature points is determined in terms of the classification ability to face and non-face images (cross validation). The matching is performed in the order of the selected feature points. It is confirmed that high rec:ognition rate is obtained by using only a small number of feat...