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Non-destructive silkworm pupa Gender classification with X-ray images using ensemble learning

Artificial Intelligence in Agriculture 2022年第1期6卷 100-110页

作者： Sania Thomas Jyothi Thomas Department of Computer Science and Engineering Christ(Deemed to be University)BangaloreIndia

Sericulture is the process of cultivating silkworms for the production of silk.High-quality production of silk without mixing with low quality is a great challenge faced in the silk production centers.One of the possibilities to overcome this issue is by separating male and female cocoons before extracting silk fibers from the cocoons as male cocoon silk fibers are finer than females.This study proposes a method for the classification of male and female cocoons with the help of X-ray images without destructing the cocoon.The study used popular single hybrid varieties FC1 and FC2 mulberry silkworm cocoons.The shape features of the pupa are considered for the classification process and were obtained without cutting the cocoon.A novel point interpolation method is used for the computation of the width and height of the cocoon.Different dimensionality reduction methods are employed to enhance the performance of the model.The preprocessed features are fed to the powerful ensemble learning method AdaBoost and used logistic regression as the base learner.This model attained a mean accuracy of 96.3%for FC1 and FC2 in cross-validation and 95.3%in FC1 and 95.1%in FC2 for external validation.

关键词： Sericulture Gender classification Stratified k-fold cross-validation Machine learning AdaBoost

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A 3D morphometric perspective for facial gender analysis and classification using geodesic path curvature features

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Computational Visual Media 2018年第1期4卷 17-32页

作者： Hawraa Abbas Yulia Hicks David Marshall Alexei I.Zhurov StephenRichmond School of Engineering Kerbala UniversityKarbolaaIraq. School of Engineering Cardiff UniversityCardiffUK. School of Computer Science and Informatics Cardiff UniversityCardiffUK. School of Dentistry Cardiff UniversityCardiffUK.

The relationship between the shape and gender of a face, with particular application to automatic Gender classification, has been the subject of significant research in recent years. Determining the gender of a face, especially when dealing with unseen examples, presents a major challenge. This is especially true for certain age groups, such as teenagers, due to their rapid development at this phase of life. This study proposes a new set of facial morphological descriptors,based on 3D geodesic path curvatures, and uses them for gender analysis. Their goal is to discern key facial areas related to gender, specifically suited to the task of Gender classification. These new curvature-based features are extracted along the geodesic path between two biological landmarks located in key facial areas.classification performance based on the new features is compared with that achieved using the Euclidean and geodesic distance measures traditionally used in gender analysis and classification. Five different experiments were conducted on a large teenage face database(4745 faces from the Avon Longitudinal Study of Parents and Children) to investigate and justify the use of the proposed curvature features. Our experiments show that the combination of the new features with geodesic distances provides a classification accuracy of 89%. They also show that nose-related traits provide the most discriminative facial feature for Gender classification, with the most discriminative features lying along the 3D face profile curve.

关键词： ALSPAC dataset Gender classification curvature features geodesic curve

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Using Multiple Views for Gait-based Gender classification

Using Multiple Views for Gait-based Gender Classification

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第26届中国控制与决策会议

作者： De Zhang Yahui Wang School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

Automatic Gender classification of an individual can be very useful in video-based surveillance systems and human-computer interaction systems. In this paper, we propose an approach to integrate information from multi-view gait at the feature level. First, gait energy images（GEI） are constructed from the video streams for different viewpoints.Then, the feature fusion is performed by putting GEI images and camera views together to generate a third-order tensor（x, y, view）. A multilinear principal component analysis is employed to reduce dimensionality of the tensor objects which integrate all views. Compared with other methods, the proposed fusion scheme shows more effective performance for multi-view gait based Gender classification.

关键词： Gait Gender classification Multi-view Fusion

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