A small look at the ear recognition process using a hybrid approach

Galdámez, Pedro Luis; Arrieta, Angélica González; Ramón, Miguel Ramón

doi:10.1016/j.jal.2015.09.004

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…In the paper aimed to discuss about recognition faces in images used fuzzy approach to extract skin region [145]. While in [146] and [147] papers discuss about ear in face images one focus on the localize ear in face and the other one focus on the prepressing techniques. And the last paper [148] presented a system for automatic spectral signature acquisition and recognition of skin from hyper spectral face imagery.…”

Section: D: Recognitionmentioning

confidence: 99%

The Landscape of Research on Skin Detectors: Coherent Taxonomy, Open Challenges, Motivations, Recommendations and Statistical Analysis, Future Directions

et al. 2019

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This paper review and analysis the literature on skin detector (SD), in order to establish the coherent taxonomy and figure out the gap on this pivotal research area. An extensive search is conducted to identify articles that deal with skin detection, skin segmentation, skin tone detector, and skin recognition issues, related techniques are reviewed comprehensively and a coherent taxonomy of these articles is established. ScienceDirect, IEEE Xplore, and Web of Science databases are checked for articles on skin detector. A total of 2803 papers are collected from 2007 to February 2018. The set comprised 173 articles. The largest portion of the papers (n = 158/173) = 91% belong to Development and Design, that is aimed to develop an approach for skin classifier into skin and non-skin. A sum total of (n = 5/173) = 3% of the papers belong to Evaluation and Framework, (n = 10/173) = 6% papers was categorized as Comparative Study. This paper discusses the open challenges, motivations and recommendations of the related works. Furthermore, state-of-the-art is a step to demonstrate the novelty of the presented study by conducted a statistical analysis for previous studies such as (Dataset, Color spaces, features, image type, and Classification techniques) as a future direction for other researchers who are interested in SD.

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Section: D: Recognitionmentioning

confidence: 99%

The Landscape of Research on Skin Detectors: Coherent Taxonomy, Open Challenges, Motivations, Recommendations and Statistical Analysis, Future Directions

et al. 2019

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“…LDA is widely used in dierent applications such as biometrics (Marcialis and Roli, 2002;Tharwat et al, 2014a), bioinformatics (Wu et al, 2009), and chemoinformatics (Mitchell, 2014). LDA is a supervised dimensionality reduction and feature extraction method (Galdámez et al, 2015). It nds the projection space that maximizes the ratio of the between-class variance, S B , to the within-class variance, S W , and hence guaranteeing maximum class separability as shown in Figure 1b (Welling, 2005).…”

Section: An Overview Of Ldamentioning

confidence: 99%

One-dimensional vs. two-dimensional based features: Plant identification approach

Tharwat

Gaber

Hassanien

2017

Journal of Applied Logic

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The number of endangered species has been increased due to shifts in the agricultural production, climate change, and poor urban planning. This lead to investigating new methods to address the problem of plant species identication/classication. In this paper, a plant identication approach using 2D digital leaves images was proposed. The approach used two features extraction methods based on one-dimensional (1D) and two-dimensional (2D) and the Bagging classier. For the 1D-based methods, Principal Component Analysis (PCA), Direct Linear Discriminant Analysis (DLDA), and PCA+LDA techniques were applied, while 2DPCA and 2DLDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classier, with the decision tree as a weak learner was used. The ve variants, i.e. PCA, PCA+LDA, DLDA, 2DPCA, and 2DLDA, of the approach were tested using the Flavia public dataset which consists of 1907 colored leaves images. The accuracy of these variants was evaluated and the results showed that the 2DPCA and 2DLDA methods were much better than using the PCA, PCA+LDA, and DLDA. Furthermore, it was found that the 2DLDA method was the best one and the increase of the weak learners of the Bagging classier yielded a better classication accuracy. Also, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup.

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Person Identification Through Ear Biometrics—A Systematic Survey

Sharma,

Seeja

2024

Lecture Notes in Networks and Systems

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A small look at the ear recognition process using a hybrid approach

Cited by 7 publications

References 19 publications

The Landscape of Research on Skin Detectors: Coherent Taxonomy, Open Challenges, Motivations, Recommendations and Statistical Analysis, Future Directions

The Landscape of Research on Skin Detectors: Coherent Taxonomy, Open Challenges, Motivations, Recommendations and Statistical Analysis, Future Directions

One-dimensional vs. two-dimensional based features: Plant identification approach

Person Identification Through Ear Biometrics—A Systematic Survey

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