This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting. LIME was initially proposed for networks with the same output classes used for training, and it employs the softmax probability to determine which regions of the image contribute the most to classification. However, in a verification setting, the classes to be recognized have not been seen during training. In addition, instead of using the softmax output, face descriptors are usually obtained from a layer before the classification layer. The model is adapted to achieve explainability via cosine similarity between feature vectors of perturbated versions of the input image. The method is showcased for face biometrics with two CNN models based on MobileNetv2 and ResNet50.

翻译：本文描述了一种将局部可解释模型无关解释（LIME）人工智能方法适配于生物特征验证场景的技术。LIME最初是针对训练阶段所用输出类别相同的网络而提出的，它利用softmax概率来确定图像中哪些区域对分类贡献最大。然而，在验证场景中，需要识别的类别在训练阶段并未出现。此外，面部特征描述符通常从分类层之前的网络层获取，而非使用softmax输出。通过计算输入图像扰动版本的特征向量之间的余弦相似度，对该方法进行了适配以实现可解释性。本文以基于MobileNetv2和ResNet50的两种CNN模型为例，展示了该方法在人脸生物特征验证中的应用效果。