Abstract
Missing, swapping, false insurance claims and reallocation of pet animals (dog) are global problems throughout the world and research done to solve this problem is minimal. Traditional biometrics and non-biometrics methods have their own boundaries and they fail to provide competent level of security to pet animal (dog). The work on animal identification based on their phenotype appearance (coat patterns) has been an active research area in recent years and automatic face recognition for dog is not reported in the literature. Dog identification needs innovative research to protect the pet animal. Therefore it is imperative to initiate research, so that future face recognition algorithm will be able to solve this important problem for identification of pet animal (like dog, cat). In this paper an attempt has been made to minimize the above mentioned problems by biometrics face recognition of dog. The contributions of this research are: 1) implementation of an existing biometrics algorithm which mitigates the effects of covariates for dogs; 2) proposed fusion based method for recognition of pet animal with 94.86% accuracy. Thus in this paper, we have tried to demonstrate that face recognition of dog can be used to recognize the dog efficiently.
Highlights
Dogs were the first pet animal to be domesticated in our society and have shared a common environment with humans for over ten thousand years
The steps involved in pet animal identification block diagram with Gaussian smoothing techniques are illustrated with (Level: 1 dog image dimension (96 × 112)), (Level: 2 dog image dimension (46 × 56)), (Level: 3 dog image dimension (23 × 28)) and (Level: 4 dog image dimension (12 × 14)) respectively four levels of Gaussian smoothing are applied so that it can adequately noise filtered while preserving discriminating texture information are shown in Figure 3 (Gaussian pyramid level 1)
The performance of Independent Component Analysis (ICA) yields identification accuracy 89.95% which is greater than PCA, Linear Discriminant Analysis (LDA) identification accuracy because it can account for more variation in the input pet animal face image compared with PCA and LDA
Summary
Dogs were the first pet animal to be domesticated in our society and have shared a common environment with humans for over ten thousand years. A phenotype is a combination of an organism’s observation characteristics or traits such as its morphology, biochemical and physiological behavior research field in computer vision and pattern recognition has repeatedly been recognized as an intellectual frontier in different applications (like pet animal and human biometrics) whose boundaries of applicability are yet to be determined One of such novel applications is known as visual animal biometrics [3]. On the other hand, using the animal biometric system faces big challenges with respect to identification accuracy, robustness as animal’s body dynamic and their body morphological traits may be controlled Driven from this need, the earliest contribution of this innovative research is to gather a database of live captured pet dog which operates as a benchmark for the proposed dog identification scheme.
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