Mukhammed Garifulla

The growing integration of companion animals, such as dogs and cats, into households and society, alongside increasing policies and laws for pet management, necessitates reliable identification systems. Traditional methods, such as collars, microchips, and tattoos, pose issues such as invasiveness, the need for specialized scanners, and the potential for loss or error. This study introduces an AI-powered biometric identification system using smartphone images to offer a convenient and accessible alternative. We present a unified framework incorporating two advanced models: Dog Nose Network Version 2 (DNNetV2) for dog nose-print recognition and Cat Face Network (CatFaceNet) for cat facial identification. DNNetV2, the second iteration of our dog nose identification model, enhanced by the Tiny Vision Transformer and a novel MagFace-based loss function, achieves 99.8% Rank-1 identification accuracy and 99.62% verification accuracy. Similarly, CatFaceNet, leveraging human facial recognition techniques and our advancements in DNNetV2, achieves 99.96% Rank-1 identification accuracy and 99.26% verification accuracy. This comprehensive system demonstrates robust, high-performance identification for both dogs and cats, with potential applications in national pet registry, pet insurance, veterinary care, and lost pet recovery. Our unified solution establishes a new benchmark in pet biometric identification, ensuring exceptional reliability and performance.

Recently, the amount of attention paid towards convolutional neural networks (CNN) in medical image analysis has rapidly increased since they can analyze and classify images faster and more accurately than human abilities. As a result, CNNs are becoming more popular and play a role as a supplementary assistant for healthcare professionals. Using the CNN on portable medical devices can enable a handy and accurate disease diagnosis. Unfortunately, however, the CNNs require high-performance computing resources as they involve a significant amount of computation to process big data. Thus, they are limited to being used on portable medical devices with limited computing resources. This paper discusses the network quantization techniques that reduce the size of CNN models and enable fast CNN inference with an energy-efficient CNN accelerator integrated into recent mobile processors. With extensive experiments, we show that the quantization technique reduces inference time by 97% on the mobile system integrating a CNN acceleration engine.