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binary feature fusion for discriminative and secure multi-biometric cryptosystems

IMAGE AND VISION COMPUTING 2017年第Feb.期58卷 254-265页

作者： Mai, Guangcan Lim, Meng-Hui Yuen, Pong C. Hong Kong Baptist Univ Dept Comp Sci Kowloon Hong Kong Peoples R China

Biometric cryptosystem has been proven to be a promising approach for template protection. Cryptosystems such as fuzzy extractor and fuzzy commitment require discriminative and informative binary biometric input to offer accurate and secure recognition. In multi-modal biometric recognition, binary features can be produced via fusing the real-valued unimodal features and binarizing the fused features. However, when the extracted features of certain modality are represented in binary and the extraction parameters are not known, real-valued features of other modalities need to be binarized and the feature fusion needs to be carried out at the binary level. In this paper, we propose a binary feature fusion method that extracts a set of fused binary features with high discriminability (small intra-user and large inter-user variations) and entropy (weak dependency among bits and high bit uniformity) from multiple sets of binary unimodal features. Unlike existing fusion methods that mainly focus on discriminability, the proposed method focuses on both feature discriminability and system security: The proposed method 1) extracts a set of weakly dependent feature groups from the multiple unimodal features;and 2) fuses each group to a bit using a mapping that minimizes the intra-user variations and maximizes the inter-user variations and uniformity of the fused bit. Experimental results on three multi-modal databases show that fused binary feature of the proposed method has both higher discriminability and higher entropy compared to the unimodal features and the fused features generated from the state-of-the-art binary fusion approaches. (C) 2016 Published by Elsevier B.V.

关键词： Biometric binary representation binary feature Multi-biometric feature fusion Template protection Cryptosystems

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Simultaneous Local binary feature Learning and Encoding for Homogeneous and Heterogeneous Face Recognition

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2018年第8期40卷 1979-1993页

作者： Lu, Jiwen Liong, Venice Erin Zhou, Jie Tsinghua Univ Natl Lab Informat Sci & Technol TNList Dept Automat State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Nanyang Technol Univ Interdisciplinary Grad Sch Rapid Rich Object Search ROSE Lab Singapore 639798 Singapore

In this paper, we propose a simultaneous local binary feature learning and encoding (SLBFLE) approach for both homogeneous and heterogeneous face recognition. Unlike existing hand-crafted face descriptors such as local binary pattern (LBP) and Gabor features which usually require strong prior knowledge, our SLBFLE is an unsupervised feature learning approach which automatically learns face representation from raw pixels. Unlike existing binary face descriptors such as the LBP, discriminant face descriptor (DFD), and compact binary face descriptor (CBFD) which use a two-stage feature extraction procedure, our SLBFLE jointly learns binary codes and the codebook for local face patches so that discriminative information from raw pixels from face images of different identities can be obtained by using a one-stage feature learning and encoding procedure. Moreover, we propose a coupled simultaneous local binary feature learning and encoding (C-SLBFLE) method to make the proposed approach suitable for heterogenous face matching. Unlike most existing coupled feature learning methods which learn a pair of transformation matrices for each modality, we exploit both the common and specific information from heterogeneous face samples to characterize their underlying correlations. Experimental results on six widely used face datasets including the LFW, YouTube Face (YTF), FERET, PaSC, CASIA VIS-NIR 2.0, and Multi-PIE datasets are presented to demonstrate the effectiveness of the proposed methods.

关键词： Face recognition heterogeneous face matching feature learning binary feature compact feature biometrics

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SPHORB: A Fast and Robust binary feature on the Sphere

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2015年第2期113卷 143-159页

作者： Zhao, Qiang Feng, Wei Wan, Liang Zhang, Jiawan Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China Tianjin Univ Sch Comp Software Tianjin 300072 Peoples R China Civil Aviat Univ China Tianjin Key Lab Adv Signal Proc Tianjin Peoples R China

In this paper, we propose SPHORB, a new fast and robust binary feature detector and descriptor for spherical panoramic images. In contrast to state-of-the-art spherical features, our approach stems from the geodesic grid, a nearly equal-area hexagonal grid parametrization of the sphere used in climate modeling. It enables us to directly build fine-grained pyramids and construct robust features on the hexagonal spherical grid, thus avoiding the costly computation of spherical harmonics and their associated bandwidth limitation. We further study how to achieve scale and rotation invariance for the proposed SPHORB feature. Extensive experiments show that SPHORB consistently outperforms other existing spherical features in accuracy, efficiency and robustness to camera movements. The superior performance of SPHORB has also been validated by real-world matching tests.

关键词： Spherical image binary feature Panoramic image matching Geodesic grid

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binary feature Completion Algorithm Based on Distribution Constrains 2

Binary Feature Completion Algorithm Based on Distribution Co...

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2nd International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Cao, Shan Zhao, Yawei Sun, Shuwei Song, Chunyu Univ Chinese Acad Sci Big Data Anal Technol Lab Beijing Peoples R China Chengdu Qixin Tech Co Ltd Chengdu Peoples R China

ISBN: (纸本)9781728108315

The quality of features has an important influence on machine learning models. In reality, value-missing is one of the most serious problems faced by learning models. Existing research works on feature completion cared more about specific scenarios based on different types of features, and does not consider the general characteristics. As binary feature is the most basic form of features, here proposed a general completion method of it based on distribution constrains and took the quantum entanglement property in consider. To evaluate the results, a criteria based on "distribution error" was proposed. The data of the experiment were all taken from real offline consuming activities, they were pre-processed by feature engineering and went through the experiment to train the algorithm. The result shows that the algorithm is effective.

关键词： binary feature feature completion machine learning quantum entanglement distribution constrain

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ABFT: ANISOTROPIC binary feature TRANSFORM BASED ON STRUCTURE TENSOR SPACE

ABFT: ANISOTROPIC BINARY FEATURE TRANSFORM BASED ON STRUCTUR...

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20th IEEE International Conference on Image Processing (ICIP)

作者： Kim, Seungryong Yoo, Hunjae Ryu, Seungchul Ham, Bumsub Sohn, Kwanghoon Yonsei Univ Sch Elect & Elect Engn DIML Seoul 120749 South Korea

ISBN: (纸本)9781479923410

Local feature matching is a fundamental step for many computer vision applications. Recently, binary feature transforms have been popularly proposed to improve the computational efficiency while preserving high matching performance. However, it is sensitive to noise and geometrical distortion such as affine transformation. In this paper, we propose ABFT framework, composed of a noise robust feature detection and affine invariant binary feature description based on a structure tensor space. Experimental results show that ABFT outperforms other state-of-the-art feature transforms in terms of the repeatability, recognition rate, and computational time.

关键词： feature matching binary feature anisotropic structure tensor

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A binary-feature-Based Object Recognition Accelerator With 22 M-Vector/s Throughput and 0.68 G-Vector/J Energy-Efficiency for Full-HD Resolution

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2019年第7期38卷 1265-1277页

作者： Liu, Leibo Zhu, Wenping Yin, Shouyi Wei, Shaojun Tsinghua Univ Inst Microelect Beijing 100084 Peoples R China Chinese Acad Sci Inst Semiconduct Beijing 100084 Peoples R China Univ Chinese Acad Sci Beijing 100084 Peoples R China

Considering that the binary-feature-based approximate nearest neighbor (ANN) search technique has not been fully exploited to date, a multisegment binary feature-based hierarchical clustering tree model is proposed to achieve fast binary feature matching (FM). In addition, the multisegment vocabulary forest, is developed for the ease of hardware-oriented implementation. During the ANN searching process, the corresponding leaf nodes of each segment of the query feature are returned simultaneously to improve processing speed and accuracy. Furthermore, a hierarchical decomposition based on the term frequency-inverse document frequency is used to reduce the run-time search space and total memory footprint for object database storage. Finally, a fine-grained feature-level fully pipelined object recognition accelerator is implemented based on a dedicated design between FM and object scoring. The performance of the proposed object recognition accelerator is evaluated based on TSMC 65 nm CMOS technology. The accelerator achieves 22 M-vec/s and 6.8 x 10(8) vec/J in throughput and energy efficiency for full-HD resolution, respectively;these results represent a 10.6x and 9x improvement, respectively, relative to current state-of-the-art solutions. The average power consumption is 32.6 mW when operating at 200 MHz.

关键词： Approximate nearest neighbor (ANN) binary feature multisegment processing array

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ABFT: ANISOTROPIC binary feature TRANSFORM BASED ON STRUCTURE TENSOR SPACE

ABFT: ANISOTROPIC BINARY FEATURE TRANSFORM BASED ON STRUCTUR...

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IEEE International Conference on Image Processing

作者： Seungryong Kim Hunjae Yoo Seungchul Ryu Bumsub Ham Kwanghoon Sohn Digital Image Media Laboratory (DIML) School of Electrical and Electronic Engineering Yonsei University Seoul Republic of Korea

ISBN: (纸本)9781479923427

关键词： feature matching binary feature anisotropic structure tensor Anisotropy binary Tensor geometric distortion Affine transformation feature detections computational time Computational efficiency images computer vision Transform

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Multi-scale binary geometric feature description and matching for accurate registration of point clouds 4

Multi-scale binary geometric feature description and matchin...

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4th International Workshop on Pattern Recognition (IWPR)

作者： Quan, Siwen Ma, Jie Feng, Fan Yu, Kun Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan Hubei Peoples R China

ISBN: (数字)9781510631144

ISBN: (纸本)9781510631144

Point cloud registration in military scenarios is pivotal to automatic object reconstruction and recognition. This paper proposes 1) a multi-scale binary feature representation called mLoVS (multi-scale local voxelized structure) and 2) a "min-pooling" based feature matching technique for accurate registration of tank point clouds. The key insight of our method is that traditional fixed-scale feature matching methods either suffer from limited shape information or data missing caused by occlusion, while the multi-scale way provides a flexible matching choice. In addition, the binary nature of our feature representation can alleviate the increased time budget required by multi-scale feature matching. Experiments on several sets of tank point clouds confirm the effectiveness and overall superiority of our method.

关键词： Multi-scale binary feature feature matching military point clouds

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Local feature Hashing With binary Auto-Encoder for Face Recognition

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IEEE ACCESS 2020年 8卷 37526-37540页

作者： Chen, Jing Zu, Yunxiao Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China

The learning-based hashing has recently made encouraging progress in face recognition. However, most existing hashing methods disregard the discrete constraint during optimization, inducing the accumulated quantization errors. In this work, we develop an effective learning-based hashing model, namely local feature hashing with binary auto-encoder (LFH-BAE), to directly learn local binary descriptors in the Hamming space. It attempts to exploit structure factors to well reconstruct the face image from binary codes. Specifically, we first introduce a binary auto-encoder to learn a hashing function to project each face region into high-quality binary codes. Since the original problem is a tricky combinational function, we then present a softened version to decompose it into separate tractable sub-problems. Next, we propose an effective alternating algorithm based on the augmented Lagrange method (ALM) to solve these sub-problems, which helps to generate strong discriminative and excellent robust binary codes. Moreover, we utilize the discrete cyclic coordinate descent (DCC) method to optimize binary codes to reduce the loss of useful information. Lastly, we cluster and pool the obtained binary codes, and construct a histogram feature as the final face representation for each image. Extensive experimental results on four public datasets including FERET, CAS-PEAL-R1, LFW and PaSC show that our LFH-BAE is superior to most state-of-the-art face recognition algorithms.

关键词： binary auto-encoder binary feature discrete optimization face recognition feature hashing

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SHIP DETECTION FOR KOMPSAT-3A OPTICAL IMAGES USING binary featureS AND ADABOOST CLASSIFICATION

SHIP DETECTION FOR KOMPSAT-3A OPTICAL IMAGES USING BINARY FE...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Chang, Jae Young Oh, Han Lee, Seung-Jae Lee, Kwang Jae Korea Aerosp Res Inst KARI Natl Satellite Operat & Applicat Ctr Daejeon South Korea

ISBN: (纸本)9781728163741

KOMPSAT (KOrea Multi-Purpose SATellite)-3A is a high-resolution earth observation satellite equipped with an electrooptic camera and infrared sensors developed by KARL In this paper, we introduce a method of detecting ships and estimate the performance with pan-sharpened optical images of 0.55m resolution from KOMPSAT-3A. Machine learning based detection technology is essential to detect ship objects with inconsistent shapes and backgrounds, particularly in satellite images where the radiometric characteristic varies severely due to environmental factors. To cope with these inconsistencies, we introduce a ship detection algorithm using binary features and AdaBoost classification. The experimental result shows that the algorithm effectively captures the characteristics of the appearance and shows competitive detection performance.

关键词： Ship detection machine learning binary feature AdaBoost MCT LAB random fern

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