scholarly journals Modified SIFT Descriptors for Face Recognition under Different Emotions

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Nirvair Neeru ◽  
Lakhwinder Kaur
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
User Interaction ◽  
Recognition Algorithm ◽  
Scale Invariant ◽  
Distance Ratio ◽  
Japanese Female ◽  
Database Size ◽  
Fully Automatic ◽  
Scale Invariant Feature

The main goal of this work is to develop a fully automatic face recognition algorithm. Scale Invariant Feature Transform (SIFT) has sparingly been used in face recognition. In this paper, a Modified SIFT (MSIFT) approach has been proposed to enhance the recognition performance of SIFT. In this paper, the work is done in three steps. First, the smoothing of the image has been done using DWT. Second, the computational complexity of SIFT in descriptor calculation is reduced by subtracting average from each descriptor instead of normalization. Third, the algorithm is made automatic by using Coefficient of Correlation (CoC) instead of using the distance ratio (which requires user interaction). The main achievement of this method is reduced database size, as it requires only neutral images to store instead of all the expressions of the same face image. The experiments are performed on the Japanese Female Facial Expression (JAFFE) database, which indicates that the proposed approach achieves better performance than SIFT based methods. In addition, it shows robustness against various facial expressions.

scholarly journals On the Robustness of Face Recognition Algorithms Against Attacks and Bias

2020 ◽  
Vol 34 (09) ◽  
pp. 13583-13589
Author(s):  
Richa Singh ◽  
Akshay Agarwal ◽  
Maneet Singh ◽  
Shruti Nagpal ◽  
Mayank Vatsa
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Recognition Algorithm ◽  
Future Research ◽  
Research Directions ◽  
Recognition Algorithms ◽  
Future Research Directions ◽  
And Gender ◽  
Gender Variations ◽  
Very High

Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications. Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged. This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working. Different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed. We also present a discussion on the effect of bias on face recognition models and showcase that factors such as age and gender variations affect the performance of modern algorithms. The paper also presents the potential reasons for these challenges and some of the future research directions for increasing the robustness of face recognition models.

scholarly journals Analysis of Application of Cluster Descriptions in Space of Characteristic Image Features

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 52 ◽  
Author(s):  
Oleksii Gorokhovatskyi ◽  
Volodymyr Gorokhovatskyi ◽  
Olena Peredrii
Keyword(s):  
Recognition Performance ◽  
Input Image ◽  
Image Features ◽  
Additional Time ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Image Descriptors ◽  
Structural Image ◽  
Characteristic Features ◽  
Scale Invariant Feature

In this paper, we propose an investigation of the properties of structural image recognition methods in the cluster space of characteristic features. Recognition, which is based on key point descriptors like SIFT (Scale-invariant Feature Transform), SURF (Speeded Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), etc., often relating to the search for corresponding descriptor values between an input image and all etalon images, which require many operations and time. Recognition of the previously quantized (clustered) sets of descriptor features is described. Clustering is performed across the complete set of etalon image descriptors and followed by screening, which allows for representation of each etalon image in vector form as a distribution of clusters. Due to such representations, the number of computation and comparison procedures, which are the core of the recognition process, might be reduced tens of times. Respectively, the preprocessing stage takes additional time for clustering. The implementation of the proposed approach was tested on the Leeds Butterfly dataset. The dependence of cluster amount on recognition performance and processing time was investigated. It was proven that recognition may be performed up to nine times faster with only a moderate decrease in quality recognition compared to searching for correspondences between all existing descriptors in etalon images and input one without quantization.

An Improved Face Recognition Algorithm Based on SIFT and LBP

2013 ◽  
Vol 427-429 ◽  
pp. 1999-2004 ◽  
Author(s):  
Huai Ming Yang ◽  
Jin Guang Sun
Keyword(s):  
Recognition Rate ◽  
Recognition Algorithm ◽  
Image Feature ◽  
Local Similarity ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Face Images ◽  
Calculation Results ◽  
Scale Invariant Feature ◽  
Global And Local

A new face image feature extraction and recognition algorithm based on Scale Invariant Feature Transform (SIFT) and Local Linary Patterns (LBP) is proposed in this paper. Firstly, a set of keypoints are extracted from images by using the SIFT algorithm; Secondly, each keypoint is described by LBP patterns; Finally, a combination of the global and local similarity is adopted to calculate the matching results for face images. Calculation results show that the algorithm can reduce the matching dimension of feature points, improve the recognition rate and perspective; it has nice robustness against the interferences such as rotation, lighting and expression.

scholarly journals Robust Face Recognition Under Partial Occlusion Based on Local Generic Features

2021 ◽  
Vol 15 (3) ◽  
pp. 47-57
Author(s):  
Amit Kumar Yadav ◽  
Neeraj Gupta ◽  
Aamir Khan ◽  
Anand Singh Jalal
Keyword(s):  
Face Recognition ◽  
Kernel Method ◽  
Partial Occlusion ◽  
Scale Invariant ◽  
Query Image ◽  
Art Methods ◽  
Constrained Environments ◽  
The Face ◽  
Robust Face Recognition ◽  
Scale Invariant Feature

Face recognition has drawn significant attention due to its potential use in biometric authentication, surveillance, security, robotics, and so on. It is a challenging task in the field of computer vision. Although the various state-of-the-art methods of face recognition in constrained environments have achieved satisfactory results, there are still many issues which are untouched in unconstrained environments, such as partial occlusions, large pose variations, etc. In this paper, the authors have proposed an approach which utilized the local generic feature (LGF) to recognize the face in the partial occlusion by fusing features scale invariant feature transform (SIFT) and multi-block local binary pattern (MB-LBP). It also utilizes robust kernel method for classification of the query image. They have validated the effectiveness of the proposed approach on the benchmark AR face database. The experimental outcomes illustrate that the proposed approach outperformed the state-of-art methods for robust face recognition.

scholarly journals DENSE SIFT FEATURE AND LOCAL NAIVE BAYES NEAREST NEIGHBOR FOR FACE RECOGNITION

2017 ◽  
Vol 1 (28) ◽  
pp. 56-63
Author(s):  
Khanh Ngan Chau ◽  
Nghi Thanh Doan
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Human Face ◽  
Scale Invariant ◽  
Adaboost Algorithm ◽  
The Face ◽  
Benchmark Datasets ◽  
Scale Invariant Feature

Human face recognition is a technology which is widely used in life. There have been much effort on developing face recognition algorithms. In this paper, we present a new methodology that combines Haar Like Features - Cascade of Boosted Classifiers, Dense Scale-Invariant Feature Transform (DSIFT), Local Naive Bayes Nearest Neighbor (LNBNN) algorithm for the recognition of human face. We use Haar Like Features and the combination  of AdaBoost algorithm and Cascade stratified model to detect and extract the face image, the DSIFT descriptors of the image are computed only for the aligned and cropped face image.Then, we apply the LNBNN algorithms for object recognition. Numerical testing on several benchmark datasets using our proposed method for facerecognition gives the better results than other methods. The accuracies obtained by LNBNN method is 99.74 %.

Human Head Portrait Feature Extraction Based on SIFT

2014 ◽  
Vol 644-650 ◽  
pp. 4322-4324
Author(s):  
Xue Ding ◽  
Shun Han ◽  
Hong Hong Yang ◽  
Xiao Feng Wang ◽  
Kun Wu Yang
Keyword(s):  
Feature Extraction ◽  
Image Recognition ◽  
Recognition Performance ◽  
Human Head ◽  
Human Vision ◽  
Practical Application ◽  
Scale Invariant ◽  
The Arts ◽  
Scale Invariant Feature ◽  
Advanced Computer

As the number of students who attend the arts exams has been growing, each admission institutions need to score the human head portraits of art sketch with the number of ten thousand and even 100 thousand. Thus it is an innovative research on how to conduct image recognition with the help of advanced computer technology. Image Recognition Technology is to give the computer the intelligence of human vision, so that the computer can quickly and accurately recognize the object on the input images. However, in the recognition process such factors as light, rotation and shield increase the difficulty of identifying the human head portrait images. In order to get better recognition performance, this paper studies the feature extraction of human head portrait based on SIFT (Scale Invariant Feature Transform). From the practical application, it can be seen that the approach proposed in this paper is feasible and is of good recognition performance.

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