3D Face Recognition Based on Symbolic FDA Using SVM Classifier with Similarity and Dissimilarity Distance Measure

2017 ◽  
Vol 31 (04) ◽  
pp. 1756006 ◽  
Author(s):  
Manjunatha Hiremath ◽  
P. S. Hiremath
Keyword(s):  
Face Recognition ◽  
Distance Measure ◽  
Svm Classifier ◽  
Depth Information ◽  
3D Face Recognition ◽  
Emotional States ◽  
Range Images ◽  
Human Face ◽  
Biometric Technology ◽  
3D Face

Human face images are the basis not only for person recognition, but for also identifying other attributes like gender, age, ethnicity, and emotional states of a person. Therefore, face is an important biometric identifier in the law enforcement and human–computer interaction (HCI) systems. The 3D human face recognition is emerging as a significant biometric technology. Research interest into 3D face recognition has increased during recent years due to availability of improved 3D acquisition devices and processing algorithms. A 3D face image is represented by 3D meshes or range images which contain depth information. In this paper, the objective is to propose a new 3D face recognition method based on radon transform and symbolic factorial discriminant analysis using KNN and SVM classifier with similarity and dissimilarity measures, which are applied on 3D facial range images. The experimentation is done using three publicly available databases, namely, Bhosphorus, Texas and CASIA 3D face database. The experimental results demonstrate the effectiveness of the proposed method.

Complement component face space for 3D face recognition from range images

2020 ◽  
Author(s):  
Koushik Dutta ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri ◽  
Ondrej Krejcar
Keyword(s):  
Face Recognition ◽  
Complement Component ◽  
3D Face Recognition ◽  
Range Images ◽  
3D Face ◽  
Face Space

A novel holistic approach for 3D face recognition from range images

2015 ◽  
pp. 191-196 ◽  
Author(s):  
Suranjan Ganguly ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri
Keyword(s):  
Face Recognition ◽  
Holistic Approach ◽  
3D Face Recognition ◽  
Range Images ◽  
3D Face

scholarly journals Deep Residual Feature Quantization for 3D Face Recognition

2021 ◽  
Author(s):  
walid Hariri ◽  
Marwa Zaabi
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
High Performance ◽  
Svm Classifier ◽  
3D Face Recognition ◽  
3D Face ◽  
Classification Tasks ◽  
And Storage ◽  
Performance Computing ◽  
Do So

Abstract 3D face recognition (FR) has been successfully applied using Convolutional neural networks (CNN) which have demonstrated stunning results in diverse computer vision and image classification tasks. Learning CNNs, however, need to estimate millions of parameters that expect high-performance computing capacity and storage. To deal with this issue, we propose an efficient method based on the quantization of residual features extracted from ResNet-50 pre-trained model. The method starts by describing each 3D face using a convolutional feature extraction block, and then apply the Bag-of-Features (BoF) paradigm to learn deep neural networks (we call it Deep BoF). To do so, we apply Radial Basis Function (RBF) neurons to quantize the deep features extracted from the last convolutional layers. An SVM classifier is then applied to classify faces according to their quantized term vectors. The obtained model is lightweight compared to classical CNN and it allows classifying arbitrary-sized images. The experimental results on the FRGCv2 and Bosphorus datasets show the powerful of our method compared to state of the art methods.

scholarly journals 3D FACE RECOGNITION FROM RANGE IMAGES BASED ON CURVATURE ANALYSIS

2014 ◽  
Vol 04 (03) ◽  
pp. 748-753 ◽  
Author(s):  
Suranjan Ganguly ◽  
◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri ◽  
◽  
...  
Keyword(s):  
Face Recognition ◽  
3D Face Recognition ◽  
Range Images ◽  
3D Face ◽  
Curvature Analysis

scholarly journals Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Naeem Ratyal ◽  
Imtiaz Ahmad Taj ◽  
Muhammad Sajid ◽  
Anzar Mahmood ◽  
Sohail Razzaq ◽  
...  
Keyword(s):  
Face Recognition ◽  
Superior Performance ◽  
Support Vector ◽  
Complex Nature ◽  
Alignment Algorithm ◽  
3D Face Recognition ◽  
3D Face ◽  
Norm Minimization ◽  
Wide Range ◽  
The Face

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.

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