A noise-robust semi-supervised dimensionality reduction method for face recognition

Optik ◽  
2018 ◽  
Vol 157 ◽  
pp. 858-865 ◽  
Author(s):  
Haitao Gan
Keyword(s):  
Face Recognition ◽  
Dimensionality Reduction ◽  
Reduction Method ◽  
Noise Robust ◽  
Dimensionality Reduction Method

A supervised dimensionality reduction method-based sparse representation for face recognition

2016 ◽  
Vol 64 (8) ◽  
pp. 799-806 ◽  
Author(s):  
Xinxin Zhang ◽  
Peng Yali ◽  
Shigang Liu ◽  
Jie Wu ◽  
Pingan Ren
Keyword(s):  
Face Recognition ◽  
Dimensionality Reduction ◽  
Sparse Representation ◽  
Reduction Method ◽  
Dimensionality Reduction Method

A Supervised Linear Dimensionality Reduction Method for Face Recognition

2013 ◽  
Vol 8 (1) ◽  
pp. 189-197
Author(s):  
Li Guo ◽  
Zhonglong Zheng ◽  
Jiong Jia ◽  
Haixin Zhang ◽  
Fangmei Fu
Keyword(s):  
Face Recognition ◽  
Dimensionality Reduction ◽  
Reduction Method ◽  
Linear Dimensionality Reduction ◽  
Dimensionality Reduction Method

scholarly journals Inhibition of Occluded Facial Regions for Distance-Based Face Recognition

2018 ◽  
Author(s):  
Daniel López Sánchez ◽  
Juan M. Corchado ◽  
Angelica González Arrieta
Keyword(s):  
Face Recognition ◽  
Dimensionality Reduction ◽  
Reduction Method ◽  
Computational Cost ◽  
Experimental Results ◽  
Classification Method ◽  
Partial Occlusion ◽  
Classical Distance ◽  
Dimensionality Reduction Method ◽  
Dissimilarity Function

This work focuses on the design and validation of a CBR system for efficient face recognition under partial occlusion conditions. The proposed CBR system is based on a classical distance-based classification method, modified to increase its robustness to partial occlusion. This is achieved by using a novel dissimilarity function which discards features coming from occluded facial regions. In addition, we explore the integration of an efficient dimensionality reduction method into the proposed framework to reduce computational cost. We present experimental results showing that the proposed CBR system outperforms classical methods of similar computational requirements in the task of face recognition under partial occlusion.

scholarly journals A model for spectroscopic food sample analysis using data sonification

2021 ◽  
Author(s):  
Hsein Kew
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Reduction Method ◽  
Real Life ◽  
Principal Component ◽  
Relevant Information ◽  
Analysis Model ◽  
Linear Discriminant ◽  
Audio Output ◽  
Dimensionality Reduction Method

AbstractIn this paper, we propose a method to generate an audio output based on spectroscopy data in order to discriminate two classes of data, based on the features of our spectral dataset. To do this, we first perform spectral pre-processing, and then extract features, followed by machine learning, for dimensionality reduction. The features are then mapped to the parameters of a sound synthesiser, as part of the audio processing, so as to generate audio samples in order to compute statistical results and identify important descriptors for the classification of the dataset. To optimise the process, we compare Amplitude Modulation (AM) and Frequency Modulation (FM) synthesis, as applied to two real-life datasets to evaluate the performance of sonification as a method for discriminating data. FM synthesis provides a higher subjective classification accuracy as compared with to AM synthesis. We then further compare the dimensionality reduction method of Principal Component Analysis (PCA) and Linear Discriminant Analysis in order to optimise our sonification algorithm. The results of classification accuracy using FM synthesis as the sound synthesiser and PCA as the dimensionality reduction method yields a mean classification accuracies of 93.81% and 88.57% for the coffee dataset and the fruit puree dataset respectively, and indicate that this spectroscopic analysis model is able to provide relevant information on the spectral data, and most importantly, is able to discriminate accurately between the two spectra and thus provides a complementary tool to supplement current methods.

scholarly journals Plant Leaf Recognition through Local Discriminative Tangent Space Alignment

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Chuanlei Zhang ◽  
Shanwen Zhang ◽  
Weidong Fang
Keyword(s):  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Tangent Space ◽  
Reduction Method ◽  
Plant Leaf ◽  
Local Patch ◽  
Dimensionality Reduction Method

Manifold learning based dimensionality reduction algorithms have been payed much attention in plant leaf recognition as the algorithms can select a subset of effective and efficient discriminative features in the leaf images. In this paper, a dimensionality reduction method based on local discriminative tangent space alignment (LDTSA) is introduced for plant leaf recognition based on leaf images. The proposed method can embrace part optimization and whole alignment and encapsulate the geometric and discriminative information into a local patch. The experiments on two plant leaf databases, ICL and Swedish plant leaf datasets, demonstrate the effectiveness and feasibility of the proposed method.

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