MENTOR: Human Perception-Guided Pretraining for Iris Presentation Detection

Incorporating human salience into the training of CNNs has boosted performance in difficult tasks such as biometric presentation attack detection. However, collecting human annotations is a laborious task, not to mention the questions of how and where (in the model architecture) to efficiently incorporate this information into model's training once annotations are obtained. In this paper, we introduce MENTOR (huMan pErceptioN-guided preTraining fOr iris pResentation attack detection), which addresses both of these issues through two unique rounds of training. First, we train an autoencoder to learn human saliency maps given an input iris image (both real and fake examples). Once this representation is learned, we utilize the trained autoencoder in two different ways: (a) as a pre-trained backbone for an iris presentation attack detector, and (b) as a human-inspired annotator of salient features on unknown data. We show that MENTOR's benefits are threefold: (a) significant boost in iris PAD performance when using the human perception-trained encoder's weights compared to general-purpose weights (e.g. ImageNet-sourced, or random), (b) capability of generating infinite number of human-like saliency maps for unseen iris PAD samples to be used in any human saliency-guided training paradigm, and (c) increase in efficiency of iris PAD model training. Sources codes and weights are offered along with the paper.Comment: 8 pages, 3 figure

Teaching AI to Teach: Leveraging Limited Human Salience Data Into Unlimited Saliency-Based Training

Machine learning models have shown increased accuracy in classification tasks when the training process incorporates human perceptual information. However, a challenge in training human-guided models is the cost associated with collecting image annotations for human salience. Collecting annotation data for all images in a large training set can be prohibitively expensive. In this work, we utilize ''teacher'' models (trained on a small amount of human-annotated data) to annotate additional data by means of teacher models' saliency maps. Then, ''student'' models are trained using the larger amount of annotated training data. This approach makes it possible to supplement a limited number of human-supplied annotations with an arbitrarily large number of model-generated image annotations. We compare the accuracy achieved by our teacher-student training paradigm with (1) training using all available human salience annotations, and (2) using all available training data without human salience annotations. We use synthetic face detection and fake iris detection as example challenging problems, and report results across four model architectures (DenseNet, ResNet, Xception, and Inception), and two saliency estimation methods (CAM and RISE). Results show that our teacher-student training paradigm results in models that significantly exceed the performance of both baselines, demonstrating that our approach can usefully leverage a small amount of human annotations to generate salience maps for an arbitrary amount of additional training data.Comment: 12 pages, 8 figure