基于机器学习的视频指纹识别

Video-based fingerprint verification using machine learning

把视频应用于指纹识别,定义指纹视频的内部相似性(inside similarity,SI)和一对待匹配指纹视频的外部相似性(outside similarity,SO),计算两个视频的匹配分数来表示它们的相似性,大大提高了自动指纹识别系统的识别率。为寻求更好的识别效果,提出把一次匹配结果作为一个样本,将SI和SO作为一个样本的两个特征的新思路,把判断一次匹配是同源匹配还是异源匹配问题转化为对具有二维特征(SI,SO)的样本进行分类的问题。在样本集上应用常见的机器学习算法,对每次的匹配结果进行分类。在两组样本集上的实验结果为应用机器学习算法得到的最低错误率分别为0.170 4%和0.110 6%,而使用阈值得到的最低错误率分别为0.222 9%和0.1700%。结果表明,相比使用阈值来区分指纹同、异源的方法,应用机器学习算法不仅提高了识别率,而且省去了计算两个视频的匹配分数时对参数和阈值的复杂选取过程。

Fingerprint video was utilized for fingerprint verification.Inside similarity（SI） and outside similarity（SO） were defined and used to calculate the final matching score of two fingerprint videos.A new idea was proposed to acquire the optimized performance of fingerprint verification.A matching result with two features SI and SO was viewed as a sample.The task of verifying whether two fingerprint videos are genuine matching or impostor matching,was converted to a classification task of samples with two-dimensional features（SI,SO）.In addition,machine learning algorithms were adopted to classify every matching result.Experimental results showed that the minimum error rates calculated through the method of machine learning algorithms were 0.1704% and 0.1106% while those calculated through the method of using the threshold were 0.2229% and 0.1700%.The accuracy of video-based fingerprint verification was significantly improved by using the machine learning algorithm compared to the results by using the threshold.And the current method avoided the complex process of selecting parameters and thresholds.