Animal Biometrics

Kumar, Santosh; Singh, Sanjay Kumar; Singh, Amit Kumar

doi:10.1007/978-981-10-7956-6

Cited by 10 publications

(4 citation statements)

References 0 publications

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“…Its scope, aims and methodologies are well established and widely used. For more details, one may consult Introduction to Biometrics by Jain et al [31], Encyclopedia of Biometrics by Li and Jain [32], or Animal Biometrics by Kumar et al [33]. These books go into detail on how to take ROIs and how to create templates and perform matching, etc.…”

Section: Algorithmic Verificationmentioning

confidence: 99%

The Formation and Invariance of Canine Nose Pattern of Beagle Dogs from Early Puppy to Young Adult Periods

Choi

Lee

Shin

et al. 2021

Animals

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The formation and invariance of the canine nose pattern is studied. Nose images of ten beagle dogs were collected for ten months from month two to month eleven. The nose patterns in these images are examined visually and by a biometric algorithm. It is found that the canine nose pattern is fully formed at the end of the second month since birth and remains invariant until the end of the eleventh month. This study also strongly indicates that the canine nose pattern can be used as a unique biometric marker for each individual dog.

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Section: Algorithmic Verificationmentioning

confidence: 99%

The Formation and Invariance of Canine Nose Pattern of Beagle Dogs from Early Puppy to Young Adult Periods

Choi

Lee

Shin

et al. 2021

Animals

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“…These local features will be as an entry to the recognizing or matching step. Principal Component Analysis (PCA) is one of the methods to extract the holistic feature from the image [14][15][16][17][18][19]. The PCA algorithm is used in the extraction of features in many applications such as pattern recognition, machine learning, image retrieval, face recognition and others [20].…”

Section: Face Recognition Based On Principal Component Analysismentioning

confidence: 99%

Performance Enhancement of Face Recognition under High-Density Noise Using PCA and De-Noising Technique

Habeeb¹

2020

IHJPAS

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There are many techniques for face recognition which compare the desired face image with a set of faces images stored in a database. Most of these techniques fail if faces images are exposed to high-density noise. Therefore, it is necessary to find a robust method to recognize the corrupted face image with a high density noise. In this work, face recognition algorithm was suggested by using the combination of de-noising filter and PCA. Many studies have shown that PCA has ability to solve the problem of noisy images and dimensionality reduction. However, in cases where faces images are exposed to high noise, the work of PCA in removing noise is useless, therefore adding a strong filter will help to improve the performance of recognizing faces in the case of existing high-density noise in faces images. In this paper, Median filter, Hybrid Median Filter, Adaptive Median filter, and Adaptive Weighted Mean Filter were used to remove the noise from the faces images, and they were compared in order to use the best of these filters as a pre-processing step before the face recognition process. Experimental results showed that the Adaptive Weighted Mean Filter gave better results compared with the other filters. Thus, the performance of face recognition process was improved under high-density noise using the Adaptive Weighted Mean Filter and Principal Component Analysis. For the corrupted images by 90 % noise density, Recognition rate by using Median Filter reached 0% and 33% by using Hybrid Median Filter. While Recognition rate by using the Adaptive Median Filter and Adaptive Weighted Mean Filter reached 100%.

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“…With 97.4% accuracy, the k-NN model learns and categorizes ApSnet segmented image features [44]. The first derivative spectrum is used in k-NN classification [45]. The new cow recognition system uses hybrid texture and muzzle pattern features to identify and categorize cow breeds.…”

Section: Introductionmentioning

confidence: 99%

Meat Texture Image Classification Using the Haar Wavelet Approach and a Gray-Level Co-Occurrence Matrix

Kiswanto,

Hadiyanto,

Sediyono

2024

ASI

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This research aims to examine the use of image processing and texture analysis to find a more reliable and efficient solution for identifying and classifying types of meat, based on their texture. The method used involves the use of feature extraction, Haar wavelet, and gray-level co-occurrence matrix (GLCM) (with angles of 0°, 45°, 90°, and 135°), supported by contrast, correlation, energy, homogeneity, and entropy matrices. The test results showed that the k-NN algorithm excelled at identifying the texture of fresh (99%), frozen (99%), and rotten (96%) meat, with high accuracy. The GLCM provided good results, especially on texture images of fresh (183.21) and rotten meat (115.79). The Haar wavelet results were lower than those of the k-NN algorithm and GLCM, but this method was still useful for identifying texture images of fresh meat (89.96). This research development is expected to significantly increase accuracy and efficiency in identifying and classifying types of meat based on texture in the future, reducing human error and aiding in prompt evaluation.

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Animal Biometrics

Cited by 10 publications

References 0 publications

The Formation and Invariance of Canine Nose Pattern of Beagle Dogs from Early Puppy to Young Adult Periods

The Formation and Invariance of Canine Nose Pattern of Beagle Dogs from Early Puppy to Young Adult Periods

Performance Enhancement of Face Recognition under High-Density Noise Using PCA and De-Noising Technique

Meat Texture Image Classification Using the Haar Wavelet Approach and a Gray-Level Co-Occurrence Matrix

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