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首页> 外文期刊>Multimedia Tools and Applications >Where is my puppy? Retrieving lost dogs by facial features
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Where is my puppy? Retrieving lost dogs by facial features

机译：我的小狗在哪里？通过面部特征找回丢失的狗

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摘要

A pet that goes missing is among many people's worst fears: a moment of distraction is enough for a dog or a cat wandering off from home. Some measures help matching lost animals to their owners; but automated visual recognition is one that although convenient, highly available, and low-cost - is surprisingly overlooked. In this paper, we inaugurate that promising avenue by pursuing face recognition for dogs. We contrast four ready-to-use human facial recognizers (EigenFaces, FisherFaces, LBPH, and a Sparse method) to two original solutions based upon convolutional neural networks: BARK (inspired in architecture-optimized networks employed for human facial recognition) and WOOF (based upon off-the-shelf OverFeat features). Human facial recognizers perform poorly for dogs (up to 60.5 % accuracy), showing that dog facial recognition is not a trivial extension of human facial recognition. The convolutional network solutions work much better, with BARK attaining up to 81.1 % accuracy, and WOOF, 89.4 %. The tests were conducted in two datasets: Flickr-dog, with 42 dogs of two breeds (pugs and huskies); and Snoopybook, with 18 mongrel dogs.
机译：失踪的宠物是许多人最担心的事情：分心的时刻足以使狗或猫从家中流浪。一些措施有助于将失物招领给主人。但是自动视觉识别是一种尽管方便，高度可用且成本低廉的方法，但却令人惊讶地被忽略了。在本文中，我们通过追求狗的面部识别技术开创了这一有前途的途径。我们将四种现成的人类面部识别器（EigenFaces，FisherFaces，LBPH和一种稀疏方法）与基于卷积神经网络的两个原始解决方案进行了对比：BARK（受人类面部识别所用的体系结构优化网络的启发）和WOOF（基于现成的OverFeat功能）。人脸识别器对狗的表现不佳（准确度高达60.5％），表明狗脸识别并不是人脸识别的重要扩展。卷积网络解决方案工作得更好，BARK的精度高达81.1％，WOOF的精度高达89.4％。测试是在两个数据集中进行的：Flickr-dog，有42个两个品种的狗（哈士奇犬和哈士奇犬）；和史努比书，有18只杂种狗。

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