2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)最新文献_第4页

Learning Light Field Reconstruction from a Single Coded Image 从单个编码图像学习光场重建

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.142

Anil Kumar Vadathya, Saikiran Cholleti, Gautham Ramajayam, Vijayalakshmi Kanchana, K. Mitra

引用次数: 12

A News-Driven Recurrent Neural Network for Market Volatility Prediction 基于新闻驱动的递归神经网络预测市场波动

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.35

Peikang Lin, Xianjie Mo, Guidong Lin, Liwen Ling, Tingting Wei, W. Luo

引用次数: 7

Handwritten Chinese Character Blind Inpainting with Conditional Generative Adversarial Nets 基于条件生成对抗网络的手写体汉字盲涂

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.60

Zhaobai Zhong, Fei Yin, Xu-Yao Zhang, Cheng-Lin Liu

引用次数: 5

Gait Analysis Using Shadow Motion 基于阴影运动的步态分析

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.32

Pradeep Kumar, Rajkumar Saini, Chaitanya Sai Tumma, P. Roy, D. P. Dogra

{"title":"Gait Analysis Using Shadow Motion","authors":"Pradeep Kumar, Rajkumar Saini, Chaitanya Sai Tumma, P. Roy, D. P. Dogra","doi":"10.1109/ACPR.2017.32","DOIUrl":"https://doi.org/10.1109/ACPR.2017.32","url":null,"abstract":"Gait is considered as one of the biometric traits that does not require physical interaction with machines and can be performed at a distance from the computing device. However, majority of the gait recognition systems require the subjects to be monitored in constrained environment within the viewing field of the capturing device. Such systems may fail to recognize a few of the features when the interaction environment is changed or when the body occlusion occurs due to position variations, clothing or belongings. Moreover, the walking style of a user may vary when engaged in different activities such as listening to music, playing games, fast walking, etc. In this paper, we propose a new approach of human gait recognition using Shadow motion sensor, a full body sensor unit. The framework is able to identify users robustly despite changes in their appearances. The device uses a combination of accelerometer, gyroscope and magnetometer sensors for collecting gait features. The identification process is performed using a Random Forest based classification scheme by varying number of trees. A set of users comprising with 23 males and females have participated in the data collection and they have performed four different types of walks including, normal-walk, fastwalk, walking while listening to music and walking while watching video on mobile. An average accuracy of 87.68% has been recorded in all walk scenarios. Results reveal that the proposed study can be used as a stepping stone to design robust gait biometric systems with the help of contact less sensors.","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129176413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Age-Invariant Person Identification by Segmentation Verification of Face Image 基于人脸图像分割验证的年龄不变人识别

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.157

Yuta Somada, W. Ohyama, T. Wakabayashi

{"title":"Age-Invariant Person Identification by Segmentation Verification of Face Image","authors":"Yuta Somada, W. Ohyama, T. Wakabayashi","doi":"10.1109/ACPR.2017.157","DOIUrl":"https://doi.org/10.1109/ACPR.2017.157","url":null,"abstract":"Face recognition has been a major research theme over the last two decades. There are several problems to be solved to improve the performance of face recognition. Such major problems involve appearance variation due to pose, illumination, expression, and aging. In particular, aging includes internal and external factors that cause facial appearance variation and, consequently, it is the most difficult problem to handle. In this paper, we propose a face recognition method that is robust against facial appearance variation due to aging. The proposed method employs segmentation verification of frontal face images that consists of the following three steps. (1) Face image segmentation generates three regional subimages from the input face image. (2) A matching score is calculated using gradient features from a pair consisting of the input image and a registered image for each of the three generated subimages and original (whole face) image. We obtain four matching scores. (3) The verifying classifier evaluates the matching score vector formed of the matching scores calculated for each of the four images and predicts the a posteriori probability that two matching images belong to the same person. The results of an experimental evaluation with the FGNET and MORPH face aging datasets clarify the effectiveness of the proposed method for age invariant face recognition","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128534678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deep Learning Framework for Segmentation of Retinal Layers from OCT Images 一种基于OCT图像的视网膜层分割深度学习框架

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.121

Karthik Gopinath, Samrudhdhi B. Rangrej, J. Sivaswamy

引用次数: 27

Investigating the Stacked Phonetic Bottleneck Feature for Speaker Verification with Short Voice Commands 短语音命令对说话人验证的堆叠语音瓶颈特征研究

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.74

Yichi Huang, Yuexian Zou, Yi Liu

{"title":"Investigating the Stacked Phonetic Bottleneck Feature for Speaker Verification with Short Voice Commands","authors":"Yichi Huang, Yuexian Zou, Yi Liu","doi":"10.1109/ACPR.2017.74","DOIUrl":"https://doi.org/10.1109/ACPR.2017.74","url":null,"abstract":"Text-dependent speaker verification (SV) with short voice command (SV-SVC) has increasing demand in many applications. Different from conventional SV, SV-SVC usually uses short fixed voice commands for user-friendly purpose, which causes technical challenges compared with conventional text-dependent SV using fixed phrases (SV-FP). Research results show that the mainstream SV techniques are not able to provide good performance for SV-SVC tasks since they suffer from strongly lexical-overlapping and short utterance length problems. In this paper, we propose to fully explore the acoustic features and contextual information of the phonetic units to obtain better speaker-utterance related information representation for i-vector based SV-SVC systems. Specifically, instead of using MFCC only, the frame-based phonetic bottleneck (PBN) feature extracted from a phonetic bottleneck neural network (PBNN), the stacked phonetic bottleneck (SBN) feature, the cascaded feature of PBN and MFCC, the cascaded feature of SBN and MFCC (SBNF+MFCC) are extracted for developing i-vector based SV-SVC systems. Intensive experiments on the benchmark database RSR2015 have been conducted to evaluate the performance of our proposed ivector SV-SVC systems. It is encouraged that the contextual information learnt from stacked PBNN does help and proposed ivector SV-SVC system with (SBNF+MFCC) outperforms under experimental conditions.","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115681531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Reweighted Low Rank Representation Based on Fractional-Order Function 基于分数阶函数的重加权低秩表示

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.132

Yiqiang Zhai, Zexuan Ji

引用次数: 0

Sequence-to-Sequence Learning for Human Pose Correction in Videos 视频中人体姿势校正的序列到序列学习

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.126

S. Swetha, V. Balasubramanian, C. V. Jawahar

引用次数: 2

Enhanced Adaptive Locality Preserving Projections for Face Recognition 增强的自适应局部保持投影人脸识别

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.123

Jun Fan, Qiaolin Ye, Ning Ye

引用次数: 6