22,634 research outputs found
Automatic human face detection for content-based image annotation
In this paper, an automatic human face detection approach using colour analysis is applied for content-based image annotation. In the face detection, the probable face region is detected by adaptive boosting algorithm, and then combined with a colour filtering classifier to enhance the accuracy in face detection. The initial experimental benchmark shows the proposed scheme can be efficiently applied for image annotation with higher fidelity
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
Robust face detection in the wild is one of the ultimate components to
support various facial related problems, i.e. unconstrained face recognition,
facial periocular recognition, facial landmarking and pose estimation, facial
expression recognition, 3D facial model construction, etc. Although the face
detection problem has been intensely studied for decades with various
commercial applications, it still meets problems in some real-world scenarios
due to numerous challenges, e.g. heavy facial occlusions, extremely low
resolutions, strong illumination, exceptionally pose variations, image or video
compression artifacts, etc. In this paper, we present a face detection approach
named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN)
to robustly solve the problems mentioned above. Similar to the region-based
CNNs, our proposed network consists of the region proposal component and the
region-of-interest (RoI) detection component. However, far apart of that
network, there are two main contributions in our proposed network that play a
significant role to achieve the state-of-the-art performance in face detection.
Firstly, the multi-scale information is grouped both in region proposal and RoI
detection to deal with tiny face regions. Secondly, our proposed network allows
explicit body contextual reasoning in the network inspired from the intuition
of human vision system. The proposed approach is benchmarked on two recent
challenging face detection databases, i.e. the WIDER FACE Dataset which
contains high degree of variability, as well as the Face Detection Dataset and
Benchmark (FDDB). The experimental results show that our proposed approach
trained on WIDER FACE Dataset outperforms strong baselines on WIDER FACE
Dataset by a large margin, and consistently achieves competitive results on
FDDB against the recent state-of-the-art face detection methods
Face Detection with Effective Feature Extraction
There is an abundant literature on face detection due to its important role
in many vision applications. Since Viola and Jones proposed the first real-time
AdaBoost based face detector, Haar-like features have been adopted as the
method of choice for frontal face detection. In this work, we show that simple
features other than Haar-like features can also be applied for training an
effective face detector. Since, single feature is not discriminative enough to
separate faces from difficult non-faces, we further improve the generalization
performance of our simple features by introducing feature co-occurrences. We
demonstrate that our proposed features yield a performance improvement compared
to Haar-like features. In addition, our findings indicate that features play a
crucial role in the ability of the system to generalize.Comment: 7 pages. Conference version published in Asian Conf. Comp. Vision
201
An Automatic Human Face Detection Method
This article contains a proposal for an automatic human face detection method, that tries to join several theories proposed by different authors. The method is based on detection of shape features (eye pairs) and skin color. The method assumes certain circumstances and constraints, respectively. Therefore it is not applicable universally. Given the constraints, it is effective enough for applications where fast execution is required. Test results are given and at the end some directives for future work are discussed
- …