Heterogeneous Face Recognition with CNNs
نویسندگان
چکیده
Heterogeneous face recognition aims to recognize faces across different sensor modalities. Typically, gallery images are normal visible spectrum images, and probe images are infrared images or sketches. Recently significant improvements in visible spectrum face recognition have been obtained by CNNs learned from very large training datasets. In this paper, we are interested in the question to what extent the features from a CNN pre-trained on visible spectrum face images can be used to perform heterogeneous face recognition. We explore different metric learning strategies to reduce the discrepancies between the different modalities. Experimental results show that we can use CNNs trained on visible spectrum images to obtain results that are on par or improve over the stateof-the-art for heterogeneous recognition with near-infrared images and sketches.
منابع مشابه
Improving Heterogeneous Face Recognition with Conditional Adversarial Networks
Heterogeneous face recognition between color image and depth image is a much desired capacity for real world applications where shape information is looked upon as merely involved in gallery. In this paper, we propose a cross-modal deep learning method as an effective and efficient workaround for this challenge. Specifically, we begin with learning two convolutional neural networks (CNNs) to ex...
متن کاملA Discriminative Feature Learning Approach for Deep Face Recognition
Convolutional neural networks (CNNs) have been widely used in computer vision community, significantly improving the stateof-the-art. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new supervision signal, called center loss, for ...
متن کاملSeqFace: Make full use of sequence information for face recognition
Deep convolutional neural networks (CNNs) have greatly improved the Face Recognition (FR) performance in recent years. Almost all CNNs in FR are trained on the carefully labeled datasets containing plenty of identities. However, such high-quality datasets are very expensive to collect, which restricts many researchers to achieve state-of-the-art performance. In this paper, we propose a framewor...
متن کاملFace Attribute Prediction Using Off-The-Shelf Deep Learning Networks
Predicting attributes from face images in the wild is a challenging computer vision problem. To automatically describe face attributes from face containing images, traditionally one needs to cascade three technical blocks — face localization, facial descriptor construction, and attribute classification — in a pipeline. As a typical classification problem, face attribute prediction has been addr...
متن کاملTo Frontalize or Not To Frontalize: A Study of Face Pre-Processing Techniques and Their Impact on Recognition
Face recognition performance has improved remarkably in the last decade. Much of this success can be attributed to the development of deep learning techniques such as convolutional neural networks (CNNs). While CNNs have pushed the state-of-the-art forward, their training process requires a large amount of clean and correctly labelled training data. If a CNN is intended to tolerate facial pose,...
متن کامل