Face Recognition Using Gabor Wavelet Features with PCA and KPCA - A Comparative Study

Vinay, A.; Shekhar, Vinay S.; Murthy, K. N. Balasubramanya; Natarajan, S. P.

doi:10.1016/j.procs.2015.07.434

Cited by 47 publications

(31 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Four results showed that the method in this paper was better than the other ones. Advances in Multimedia 9 69.3333 (5,14) 59.3333 (4,12) 63.3333 (5,14) 59.3333 k=3 (4,12,14) 78 (5,(13)(14) 56.6667 (4,12,16) 76 (4,12,14) 64.6667 k=4 (4,7,12,14) 75.3333 (5,(13)(14)(15) 68.6667 (4,9,12,16) 73.3333 (4,9,12,14) 72 k=5 (2-5,7)…”

Section: Discussionmentioning

confidence: 99%

“…Correct classification rates of CFS-Spearman algorithm in cases of 1feature, 2-features, to 13-features were better than or equal to original CFS, mRMR, and ℓp-norm (p=0.9). When more than 9 features were selected, the correct classification rate reached the highest value 84.2%, and the 9-features set included features 3,5,6,7,9,11,12,13, and 14 corresponding to Table 4.…”

Section: Experiments Analyzing For Feature Selectionmentioning

confidence: 99%

“…Different feature set might be selected by different feature selection or dimension reduction methods. The feature dimension reduction algorithms, such as PCA [4,5], FDA [6], and KPCA [7,8], would reduce the feature number, while feature selection algorithms would select the optimal feature set from all the original features. Compared with the dimension reduction algorithms, the feature selection maintains the physical meanings of the original features and this is more convenient for data and relationship between features and videos' popularity analysis [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Commercial Video Evaluation via Low-Level Feature Extraction and Selection

Lun

Wang

et al. 2018

Advances in Multimedia

View full text Add to dashboard Cite

To discover the influence of the commercial videos’ low-level features on the popularity of the videos, the feature selection method should be used to get the video features influencing the videos’ evaluation mostly after analyzing the source data and the audiences’ evaluations of the videos. After extracting the low-level features of the videos, this paper improved the Correlation-Based Feature Selection (CFS) method which is widely used and proposed an algorithm named CFS-Spearmen which combined the Spearmen correlation coefficient and the classical CFS to select features. The 4 datasets in UCI machine learning database were employed as the experiment data. The experiment results were compared with the results using traditional CFS, Minimum Redundancy and Maximum Relevance (mRMR). The SVM was used to test the method in this paper. Finally, the proposed method was used in commercial videos’ feature selection and the most influential feature set was obtained.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Experiments Analyzing For Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Commercial Video Evaluation via Low-Level Feature Extraction and Selection

Lun

Wang

et al. 2018

Advances in Multimedia

View full text Add to dashboard Cite

show abstract

“…Examples in this category include eigenface approaches such as our previous work 38 and multiresolution techniques such as wavelet transform. 7,39 Better recognition rates were achieved by applying these methods to face regions such as nose and eyes that are less sensitive to facial expression changes 40 but again finding these regions in a noisy depth map is not an easy task.…”

Section: Feature Extraction Using Curvelet Transformmentioning

confidence: 99%

Using nonlocal filtering and feature extraction approaches in three-dimensional face recognition by Kinect

Mohammadi

Gervei

2018

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

To use low-cost depth sensors such as Kinect for three-dimensional face recognition with an acceptable rate of recognition, the challenges of filling up nonmeasured pixels and smoothing of noisy data need to be addressed. The main goal of this article is presenting solutions for aforementioned challenges as well as offering feature extraction methods to reach the highest level of accuracy in the presence of different facial expressions and occlusions. To use this method, a domestic database was created. First, the noisy pixels-called holes-of depth image is removed by solving multiple linear equations resulted from the values of the surrounding pixels of the holes. Then, bilateral and block matching 3-D filtering approaches, as representatives of local and nonlocal filtering approaches, are used for depth image smoothing. Curvelet transform as a well-known nonlocal feature extraction technique applied on both RGB and depth images. Two unsupervised dimension reduction techniques, namely, principal component analysis and independent component analysis, are used to reduce the dimension of extracted features. Finally, support vector machine is used for classification. Experimental results show a recognition rate of 90% for just depth images and 100% when combining RGB and depth data of a Kinect sensor which is much higher than other recently proposed algorithms.

show abstract

“…of the relative position and distance .As a result of these geometric features easy affected by illumination, expression, posture change, the stability is bad. The other one is based on statistical feature extraction method .The principal component analysis (PCA) [2] and linear discrimination analysis (LDA) [3] as two powerful tool of feature extraction and data description has been widely used in face recognition.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Method Based on 2DLDA and SVM Optimated by PSO Algorithm

Zou¹,

Zhang²

2016

Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)

View full text Add to dashboard Cite

Abstract. Concerning the "Small Samples Size" problem in LDA algorithm and reduce the effects to the SVM face recognition rate caused by random parameters set by human. An algorithm based on combination with the PSO algorithm which was originated form artificial life and evolutionary computation to SVM's parameters election and optimization, and Wavelet Transform , two-dimensional LDA(2DLDA) was proposed. Firstly, the original images were decomposed into high-frequency and low-frequency Components by Wavelet Transform (WT). The high-frequency components were ignored, while the low-frequency components can be obtained. Then, the liner discriminant features were extracted by two-dimensional LDA (2DLDA). Finally, we use the PSO algorithm to SVM's parameters election and optimization. Experimental results based on ORL face database show the validity of the algorithm this paper proposed for face recognition and it can reach the recognition rate of 98%.

show abstract

Face Recognition Using Gabor Wavelet Features with PCA and KPCA - A Comparative Study

Cited by 47 publications

References 24 publications

Commercial Video Evaluation via Low-Level Feature Extraction and Selection

Commercial Video Evaluation via Low-Level Feature Extraction and Selection

Using nonlocal filtering and feature extraction approaches in three-dimensional face recognition by Kinect

Face Recognition Method Based on 2DLDA and SVM Optimated by PSO Algorithm

Contact Info

Product

Resources

About