85,353 research outputs found
Multi-View Face Recognition From Single RGBD Models of the Faces
This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks
Development Of Hierarchical Skin-Adaboost-Neural Network (H-Skann) For Multiface Detection In Video Surveillance System
Automatic face detection is mainly the first step for most of the face-based
biometric systems today such as face recognition, facial expression recognition, and
tracking head pose. However, face detection technology has various drawbacks caused
by challenges in indoor and outdoor environment such as uncontrolled lighting and
illumination, features occlusions and pose variation. This thesis proposed a technique
to detect multiface in video surveillance application with strategic architecture
algorithm based on the hierarchical and structural design. This technique consists of
two major blocks which are known as Face Skin Localization (FSL) and Hierarchical
Skin Area (HSA). FSL is formulated to extract valuable skin data to be processed at
the first stage of system detection, which also includes Face Skin Merging (FSM) in
order to correctly merge separated skin areas. HSA is proposed to extend the searching
of face candidates in selected segmentation area based on the hierarchical architecture
strategy, in which each level of the hierarchy employs an integration of Adaboost and
Neural Network Algorithm. Experiments were conducted on eleven types database
which consists of various challenges to human face detection system. Results reveal
that the proposed H-SKANN achieves 98.03% and 97.02% of of averaged accuracy
for benchmark database and surveillance area databases, respectively
Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition
Two approaches are proposed for cross-pose face recognition, one is based on
the 3D reconstruction of facial components and the other is based on the deep
Convolutional Neural Network (CNN). Unlike most 3D approaches that consider
holistic faces, the proposed approach considers 3D facial components. It
segments a 2D gallery face into components, reconstructs the 3D surface for
each component, and recognizes a probe face by component features. The
segmentation is based on the landmarks located by a hierarchical algorithm that
combines the Faster R-CNN for face detection and the Reduced Tree Structured
Model for landmark localization. The core part of the CNN-based approach is a
revised VGG network. We study the performances with different settings on the
training set, including the synthesized data from 3D reconstruction, the
real-life data from an in-the-wild database, and both types of data combined.
We investigate the performances of the network when it is employed as a
classifier or designed as a feature extractor. The two recognition approaches
and the fast landmark localization are evaluated in extensive experiments, and
compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table
A study on feature extraction for face recognition using Self Organizing Maps
This paper deals with the study related to the face recognition algorithms and process of developing a Self-Organizing Map (SOM) in order to carry out the process of face recognition in case of humans. Initially the paper deals with the various general steps involved in the structure and statistics based face recognition algorithms. However in the later part the key step used in the unsupervised algorithm as well as the combination of SOM and Hierarchical Self Organizing Map (HSOM) along with the aid of Gabor filters were discussed in order to carry out an efficient process of facial recognition. The feature selection criteria are also discussed in detail in order to achieve a high end result.Keywords:SOM, HSOM, Gabor filters, unsupervised learning, feature extractio
Development of Face Recognition on Raspberry Pi for Security Enhancement of Smart Home System
Nowadays, there is a growing interest in the smart home system using Internet of Things. One of the important aspect in the smart home system is the security capability which can simply lock and unlock the door or the gate. In this paper, we proposed a face recognition security system using Raspberry Pi which can be connected to the smart home system. Eigenface was used the feature extraction, while Principal Component Analysis (PCA) was used as the classifier. The output of face recognition algorithm is then connected to the relay circuit, in which it will lock or unlock the magnetic lock placed at the door. Results showed the effectiveness of our proposed system, in which we obtain around 90% face recognition accuracy. We also proposed a hierarchical image processing approach to reduce the training or testing time while improving the recognition accuracy
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