Recognizing Disguised Faces: Human and Machine Evaluation

Dhamecha, Tejas I.; Singh, Richa; Vatsa, Mayank; Kumar, Ajay

doi:10.1371/journal.pone.0099212

Cited by 111 publications

(79 citation statements)

References 53 publications

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“…The database developed in this work is first of its kind and includes ground truth for every images that illustrate presence of glasses, goggles, mustache, beard, that will enable researchers to develop advanced algorithms in accurately recognizing such real faces under disguised appearances. Secondly, we also provide a comparative performance evaluation from the two popular commercial matchers and an effective matcher for recognizing disguised faces proposed in recent journal publication [7]. The experimental results also illustrate challenges in automatically detecting face images from two commercial matchers, along with publicly available trained detector [6], and need for further work in this area.…”

Section: Our Workmentioning

confidence: 99%

Recognizing human faces under disguise and makeup

Wang

Kumar

2016

2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)

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The accuracy of automated human face recognition algorithms can significantly degrade while recognizing same subjects under make-up and disguised appearances. Increasing constraints on enhanced security and surveillance requires enhanced accuracy from face recognition algorithms for faces under disguise and/or makeup. This paper presents a new database for face images under disguised and make-up appearances the development of face recognition algorithms under such covariates. This database has 2460 images from 410 different subjects and is acquired under real environment, focuses on make-up and disguises covariates and also provides ground truth (eye glass, goggle, mustache, beard) for every image. This can enable developed algorithms to automatically quantify their capability for identifying such important disguise attribute during the face recognition. We also present comparative experimental results from two popular commercial matchers and from recent publications. Our experimental results suggest significant performance degradation in the capability of these matchers in automatically recognizing these faces. We also analyze face detection accuracy from these matchers. The experimental results underline the challenges in recognizing faces under these covariates. Availability of this new database in public domain will help to advance much needed research and development in recognizing make-up and disguised faces.

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Section: Our Workmentioning

confidence: 99%

Recognizing human faces under disguise and makeup

Wang

Kumar

2016

2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)

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“…We train the convolutional neural network by the face pictures which provided by IIITDisguise Face Database [1,2]. But its images' size is 130*150.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…We use kalman filter to combine the information to get better result than the data only be gotten by one kind of sensor. Although we train our convolutional neural network (CNN) by IIITDisguise Face Database [1,2], which provides lots of faces pictures with infrared and color version, with 130*150 pixels. But our images' size is different from the form.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning and convolutional neural network system for firefighting rescue robot

Chieh

Samani

2018

MATEC Web Conf.

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Abstract. In this paper, we combine the machine learning and neural network to build some modules for the fire rescue robot application. In our research, we build the robot legs module with Q-learning. We also finish the face detection with color sensors and infrared sensors. It is usual that image fusion is done when we want to use two kinds of sensors. Kalman filter is chosen to meet our requirement. After we finish some indispensable steps, we use sliding windows to choose our region of interest to make the system's calculation lower. The least step is convolutional neural network. We design a seven layers neural network to find the face feature and distinguish it or not.

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“…Low resolution images are also normalized in a similar manner where the inter-eye distance is normalized in proportion to the image resolution. 3 Source code available at http://www.cse.oulu.fi/CMV/Downloads-/ LPQMatlab 4 Source code available at http://labelme.csail.mit.edu-/Release3.0/browser Tools/php/matlab_toolbox.php 5 For images on which the eye-detection failed because of low resolution, normalization was performed manually. …”

Section: Algorithm 1 Co-transfer Learningmentioning

confidence: 99%

“…It is therefore desirable to build a system where surveillance cameras coupled with a face recognition algorithm can be used to automatically identify individuals from a watch-list. Along with the challenges of pose, expression, illumination [1], aging [2], disguise [3], [4], and plastic surgery [5], [6] in face recognition, matching a watch-list photograph to an image obtained from surveillance camera also requires the capability of matching across resolutions. For example, the watch-list photograph could be a high resolution image whereas the surveillance camera images are generally low resolution images.…”

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confidence: 99%

Improving Cross-Resolution Face Matching Using Ensemble-Based Co-Transfer Learning

Bhatt

Singh

Vatsa

et al. 2014

IEEE Trans. on Image Process.

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Face recognition algorithms are generally trained for matching high-resolution images and they perform well for similar resolution test data. However, the performance of such systems degrades when a low-resolution face image captured in unconstrained settings, such as videos from cameras in a surveillance scenario, are matched with high-resolution gallery images. The primary challenge, here, is to extract discriminating features from limited biometric content in low-resolution images and match it to information rich high-resolution face images. The problem of cross-resolution face matching is further alleviated when there is limited labeled positive data for training face recognition algorithms. In this paper, the problem of cross-resolution face matching is addressed where low-resolution images are matched with high-resolution gallery. A co-transfer learning framework is proposed, which is a cross-pollination of transfer learning and co-training paradigms and is applied for cross-resolution face matching. The transfer learning component transfers the knowledge that is learnt while matching high-resolution face images during training to match low-resolution probe images with high-resolution gallery during testing. On the other hand, co-training component facilitates this transfer of knowledge by assigning pseudolabels to unlabeled probe instances in the target domain. Amalgamation of these two paradigms in the proposed ensemble framework enhances the performance of cross-resolution face recognition. Experiments on multiple face databases show the efficacy of the proposed algorithm and compare with some existing algorithms and a commercial system. In addition, several high profile real-world cases have been used to demonstrate the usefulness of the proposed approach in addressing the tough challenges.

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Recognizing Disguised Faces: Human and Machine Evaluation

Cited by 111 publications

References 53 publications

Recognizing human faces under disguise and makeup

Recognizing human faces under disguise and makeup

Reinforcement learning and convolutional neural network system for firefighting rescue robot

Improving Cross-Resolution Face Matching Using Ensemble-Based Co-Transfer Learning

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