Sungjoo Suh

3D user interface combining gaze and hand gestures for large-scale display

In this paper, we present a novel attentive and immersive user interface based on gaze and hand g... more

Fingerprint Liveness Detection Using Ensemble of Local Image Quality Assessments

IS&T International Symposium on Electronic Imaging Science and Technology, Feb 14, 2016

Detecting spoofing compared to a live trait is a critical problem in the biometric authentication... more Detecting spoofing compared to a live trait is a critical problem in the biometric authentication. In this paper, we present a novel method to detect fake fingerprint attacks based on the ensemble of image quality assessments (IQAs). The key idea of the proposed method is to combine quality scores obtained from multiple local regions, which are input into the linear SVM classifier to determine whether the given fingerprint is fake or not. One important advantage of the proposed method is that, in contrast to previous approaches, it accurately identifies fake fingerprints even with small partial distortions. Moreover, the proposed method does not require any additional device. Experimental results on the mobile device show that the proposed method is effective for fingerprint liveness detection in real-world scenarios.

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Mobile LCD device with transparent infrared image sensor panel for touch and hover sensing

In this paper, we propose a novel mobile LCD device for simultaneous touch and hover sensing. A t... more In this paper, we propose a novel mobile LCD device for simultaneous touch and hover sensing. A transparent infrared image sensor panel allows the device to display contents and to detect touch and hover actions alternately. The proposed system has an advantage over touch screen devices in that a user interacts with a mobile device by not only touching the display screen but also hovering over the display screen.

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P-135: An LCD Display System with Depth-Sensing Capability Based on Coded Aperture Imaging

Sid's Digest Of Technical Papers, Jun 1, 2012

In this paper, we describe a novel LCD display system with depthsensing capability based on coded... more

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Rethinking Feature-based Knowledge Distillation for Face Recognition

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Robust Discriminative Metric Learning for Image Representation

IEEE Transactions on Circuits and Systems for Video Technology, 2018

Metric learning has attracted significant attentions in the past decades, for the appealing advan... more Metric learning has attracted significant attentions in the past decades, for the appealing advances in various realworld applications such as person re-identification and face recognition. Traditional supervised metric learning attempts to seek a discriminative metric, which could minimize the pairwise distance of within-class data samples, while maximizing the pairwise distance of data samples from various classes. However, it is still a challenge to build a robust and discriminative metric, especially for corrupted data in the real-world application. In this paper, we propose a Robust Discriminative Metric Learning algorithm (RDML) via fast low-rank representation and denoising strategy. To be specific, the metric learning problem is guided by a discriminative regularization by incorporating the pair-wise or class-wise information. Moreover, low-rank basis learning is jointly optimized with the metric to better uncover the global data structure and remove noise. Furthermore, fast low-rank representation is implemented to mitigate the computational burden and make sure the scalability on large-scale datasets. Finally, we evaluate our learned metric on several challenging tasks, e.g., face recognition/verification, object recognition, and image clustering. The experimental results verify the effectiveness of the proposed algorithm by comparing to many metric learning algorithms, even deep learning ones.

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Fingerprint Liveness Detection Using Ensemble of Local Image Quality Assessments

Electronic Imaging, 2016

Detecting spoofing compared to a live trait is a critical problem in the biometric authentication... more Detecting spoofing compared to a live trait is a critical problem in the biometric authentication. In this paper, we present a novel method to detect fake fingerprint attacks based on the ensemble of image quality assessments (IQAs). The key idea of the proposed method is to combine quality scores obtained from multiple local regions, which are input into the linear SVM classifier to determine whether the given fingerprint is fake or not. One important advantage of the proposed method is that, in contrast to previous approaches, it accurately identifies fake fingerprints even with small partial distortions. Moreover, the proposed method does not require any additional device. Experimental results on the mobile device show that the proposed method is effective for fingerprint liveness detection in real-world scenarios.

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Discriminative low-rank metric learning for face recognition

2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2015

Metric learning has attracted increasing attentions recently, because of its promising performanc... more Metric learning has attracted increasing attentions recently, because of its promising performance in many visual analysis applications. General supervised metric learning methods are designed to learn a discriminative metric that can pull all the within-class data points close enough, while pushing all the data points with different class labels far away. In this paper, we propose a Discriminative Low-rank Metric Learning method (DLML), where the metric matrix and data representation coefficients are both constrained to be low-rank. Therefore, our approach can not only dig out the redundant features with a low-rank metric, but also discover the global data structure by seeking a low-rank representation. Furthermore, we introduce a supervised regularizer to preserve more discriminative information. Different from traditional metric learning methods, our approach aims to seek low-rank metric matrix and low-rank representation in a discriminative low-dimensional subspace at the same time. Two scenarios of experiments, (e.g. face verification and face identification) are conducted to evaluate our algorithm. Experimental results on two challenging face datasets, e.g. CMU-PIE face dataset and Labeled Faces in the Wild (LFW), reveal the effectiveness of our proposed method by comparing with other metric learning algorithms.

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Investigation of Public-key Approaches to Digital Watermarking

3D user interface combining gaze and hand gestures for large-scale display

CHI '10 Extended Abstracts on Human Factors in Computing Systems, 2010

In this paper, we present a novel attentive and immersive user interface based on gaze and hand g... more

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Mobile LCD device with transparent infrared image sensor panel for touch and hover sensing

2012 IEEE International Conference on Consumer Electronics (ICCE), 2012

A fast motion deblurring based on the motion blur region search for a mobile phone

2013 IEEE International Conference on Consumer Electronics (ICCE), 2013

In this paper, we propose a fast motion deblurring technique based on a motion blur region search... more In this paper, we propose a fast motion deblurring technique based on a motion blur region search for a mobile camera phone. The technique simultaneously captures two differently exposed images in one shot. By comparing edge information from short and normal exposed images, blur regions are identified and deblurring is only performed in the identified regions based on the proposed weighted color interpolation. Further, we modify an Android camera capture flow to remain in the required camera shot-to-shot time even including the proposed technique. Experimental results using an Android camera phone demonstrate the effectiveness of our proposed method.

50.2: Adding Depth-Sensing Capability to an OLED Display System Based on Coded Aperture Imaging

SID Symposium Digest of Technical Papers, 2013

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45.2: Distinguished Paper: Novel LCD Display with a Sensible Backlight

SID Symposium Digest of Technical Papers, 2010

In this paper, we describe a novel multi‐touch LCD display architecture with hover sensing capabi... more In this paper, we describe a novel multi‐touch LCD display architecture with hover sensing capability based on infrared sensor array. The proposed display architecture has an advantage over existing multi‐touch LCD displays in that it maintains a slim form‐factor of LCD displays without loss of a display quality while it is possible to sense multiple touches and hovers simultaneously.

Efficient synthetic refocusing method from multiple coded aperture images for 3D user interaction

Computational Imaging XI, 2013

In this paper, we propose an efficient synthetic refocusing method from multiple coded aperture i... more In this paper, we propose an efficient synthetic refocusing method from multiple coded aperture images for 3D user interaction. The proposed method is applied to a flat panel display with a sensor panel which forms lens-less multi-view cameras. To capture the scene in front of the display, the modified uniformly redundant arrays (MURA) patterns are displayed on the LCD screen without the backlight. Through the imaging patterns on the LCD screen, MURA coded images are captured in the sensor panel. Instead of decoding all coded images to synthetically generate a refocused image, the proposed method only decodes one coded image corresponding to the refocusing image at a certain distance after circularly shifting and averaging all coded images. Further, based on the proposed refocusing method, the depth of an object in front of the display is estimated by finding the most focused image for each pixel through a stack of the refocused images at different depth levels. Experimental results show that the proposed method captures an object in front of the display, generates refocused images at different depth levels, and accurately determines the depth of an object including real human hands near the display

Novel LCDs with IR-sensitive backlights

Journal of the Society for Information Display, 2011

ABSTRACT In this paper, a novel multi-touch LCD architecture with hover-sensing capability is des... more ABSTRACT In this paper, a novel multi-touch LCD architecture with hover-sensing capability is described. To detect multiple touch points and hover points simultaneously, a sensitive backlight, which is a backlight integrated with an IR sensor array, is introduced. The sensitive backlight uses visible light to display contents on a display screen and is also used to detect reflected IR light from objects on or near the display screen. The captured image from the sensitive backlight is used to extract touch and hover information. The proposed display architecture maintains the slim form factor of an LCD with no loss of display quality, while making it possible to sense multiple touches and hovers simultaneously.

MTF analysis for coded aperture imaging in a flat panel display

Applications of Digital Image Processing XXXVII, 2014

In this paper, we analyze the modulation transfer function (MTF) of coded aperture imaging in a f... more In this paper, we analyze the modulation transfer function (MTF) of coded aperture imaging in a flat panel display. The flat panel display with a sensor panel forms lens-less multi-view cameras through the imaging pattern of the modified redundant arrays (MURA) on the display panel. To analyze the MTF of the coded aperture imaging implemented on the display panel, we first mathematically model the encoding process of coded aperture imaging, where the projected image on the sensor panel is modeled as a convolution of the scaled object and a function of the imaging pattern. Then, system point spread function is determined by incorporating a decoding process which is dependent on the pixel pitch of the display screen and the decoding function. Finally, the MTF of the system is derived by the magnitude of the Fourier transform of the determined system point spread function. To demonstrate the validity of the mathematically derived MTF in the system, we build a coded aperture imaging system that can capture the scene in front of the display, where the system consists of a display screen and a sensor panel. Experimental results show that the derived MTF of coded aperture imaging in a flat panel display system well corresponds to the measured MTF.

P-135: An LCD Display System with Depth-Sensing Capability Based on Coded Aperture Imaging

SID Symposium Digest of Technical Papers, 2012

In this paper, we describe a novel LCD display system with depthsensing capability based on coded... more

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Robust finger contact detection with majority quadrant search for interactive tabletop displays

2014 IEEE International Conference on Consumer Electronics (ICCE), 2014

This paper proposes a novel algorithm for robust finger blob detection in a multi-touch tabletop ... more This paper proposes a novel algorithm for robust finger blob detection in a multi-touch tabletop display where an array of multiple cameras is placed behind the display screen. The proposed algorithm reliably separates true finger touch blobs from false blobs introduced by palms and hovering hands in the presence of non-uniform illumination. The algorithm is based on majority quadrant search (MQS) which is designed to find a hand direction which leads to the removal of false blobs. Experimental results show that the proposed method reliably extract true finger touch blobs by rejecting the false blobs from a real interactive tabletop display with a multi-camera set-up.

Depth estimation from multiple coded apertures for 3D interaction

SPIE Proceedings, 2013

In this paper, we propose a novel depth estimation method from multiple coded apertures for 3D in... more In this paper, we propose a novel depth estimation method from multiple coded apertures for 3D interaction. A flat panel display is transformed into lens-less multi-view cameras which consist of multiple coded apertures. The sensor panel behind the display captures the scene in front of the display through the imaging pattern of the modified uniformly redundant arrays (MURA) on the display panel. To estimate the depth of an object in the scene, we first generate a stack of synthetically refocused images at various distances by using the shifting and averaging approach for the captured coded images. And then, an initial depth map is obtained by applying a focus operator to a stack of the refocused images for each pixel. Finally, the depth is refined by fitting a parametric focus model to the response curves near the initial depth estimates. To demonstrate the effectiveness of the proposed algorithm, we construct an imaging system to capture the scene in front of the display. The system consists of a display screen and an x-ray detector without a scintillator layer so as to act as a visible sensor panel. Experimental results confirm that the proposed method accurately determines the depth of an object including a human hand in front of the display by capturing multiple MURA coded images, generating refocused images at different depth levels, and refining the initial depth estimates.

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