想了解人脸识别和机器学习？这份论文清单帮大忙
[1] tolba a s, el-baz a h, el-harby a a a. face recognition: a literature review[j]. international journal of signal processing, 2006, 2(1):88-103.综述
[2]hinton, g.e., osindero, s. and teh, y. (2006) a fast learning algorithm for deep belief nets. neural computation, 18, 1527-1554.
hinton 2006提出的深度学习的概念
[3] krizhevsky a, sutskever i, hinton g e. imagenet classification with deep convolutional neural networks[c]// international conference on neural information processing systems. curran associates inc. 2012:1097-1105.
alexnet的论文
[4] chang f, dean j, ghemawat s, et al. bigtable: a distributed storage system for structured data[j]. acm transactions on computer systems, 2008, 26(2):1-26.
[5] dean j, ghemawat s. mapreduce: simplified data processing on large clusters[m]. acm, 2008.
[6] ghemawat s, gobioff h, leung s. file and storage systems: the google file system[j]. acm symposium on operating systems principles bolton landing, 2003, 37:29-43.
google大数据三论文
[7] yang j, lu w, waibel a. skin-color modeling and adaptation[j]. technical report, 1997, 1352:687-694.
[8] moghaddam b, pentland a. probabilistic visual learning for object representation[m]. ieee computer society, 1997.
基于肤色的人脸分割方法
[9] garcia c, tziritas g. face detection using quantized skin color regions merging and wavelet packet analysis[j]. ieee transactions on multimedia, 2002, 1(3):264-277.
先验知识模型
[10] yang j, zhang d, frangi a f, et al. two-dimensional pca: a new approach to appearance-based face representation and recognition[j]. ieee trans pattern anal mach intell, 2004, 26(1):131-137.
pca脸
[11] goldberg p, jerrum m. bounding the vapnik-chervonenkis dimension of concept classes parameterized by real numbers[c]// conference on computational learning theory. acm, 1993:361-369.
svm算法，软间隔最大化
[12] huang j, blanz v, heisele b. face recognition using component-based svm classification and morphable models[c]// international workshop on pattern recognition with support vector machines. springer-verlag, 2002:334-341.
svm与模板，识别人脸
[13] wang x. an hog-lbp human detector with partial occlusion handling[j]. proc.ieee int.conf.on computer vision kyoto japan sept, 2009, 30(2):32-39.
hog 人脸 ,lbp 人脸特征抽取
[14] mita t, kaneko t, hori o. joint haar-like features for face detection[c]// tenth ieee international conference on computer vision. 2005:1619-1626.
haar 特征，一种小波变换
[14] drucker h, cortes c, jackel l d, et al. boosting and other ensemble methods[j]. neural computation, 1989, 6(6):1289-1301.
yann lecun cnn
[15] lecun y, bengio y. convolutional networks for images, speech, and time series[m]// the handbook of brain theory and neural networks. mit press, 1998.
lecun y, bengio y. 于1998年提出的卷积神经网络的一些应用
[16] hubel d h, wiesel t n. receptive fields, binocular interaction and functional architecture in the cat’s visual cortex[j]. journal of physiology, 1962, 160(1):106-154.
卷积神经网络诞生的灵感来源，研究猫的视觉神经
[17] lecun y. lenet-5, convolutional neural networks[j].
lenet
[18] taigman y, yang m, ranzato m, et al. deepface: closing the gap to human-level performance in face verification[c]// ieee conference on computer vision and pattern recognition. ieee computer society, 2014:1701-1708.
deepface，经典的人脸识别论文，网上有大量该论文笔记
[19] he k, zhang x, ren s, et al. deep residual learning for image recognition[j]. 2015:770-778.
resnet ,state-of-the-art
[20] dechter r. learning while searching in constraint-satisfaction-problems[c]// national conference on artificial intelligence. philadelphia, pa, august 11-15, 1986. volume 1: science. dblp, 1986:178-185.
最早的深度学习概念，第一次提出的深度学习说法
[21] viola p, jones m. rapid object detection using a boosted cascade of simple features[c]// computer vision and pattern recognition, 2001. cvpr 2001. proceedings of the 2001 ieee computer society conference on. ieee, 2003:i-511-i-518 vol.1.
haar 级联人脸检测方法
[22] viola p, jones m j. robust real-time face detection[c]// computer vision, 2001. iccv 2001. proceedings. eighth ieee international conference on. ieee xplore, 2004:747.
部件人脸检测，经典论文
[23] girshick r. fast r-cnn[j]. computer science, 2015.
fast r-cnn
[24] ren s, he k, girshick r, et al. faster r-cnn: towards real-time object detection with region proposal networks.[j]. ieee transactions on pattern analysis & machine intelligence, 2015, 39(6):1137-1149.
faster-rcnn
[25] deep dense face detector spots faces from a wide range of angles[j]. biometric technology today, 2015, 2015:12.
yahoo ,ddfd，一种人脸检测方法
[26] zhang k, zhang z, li z, et al. joint face detection and alignment using multitask cascaded convolutional networks[j]. ieee signal processing letters, 2016, 23(10):1499-1503.
mtcnn，state-of-the-art 人脸检测方法
[27] slice d e. landmark coordinates aligned by procrustes analysis do not lie in kendall’s shape space.[j]. systematic biology, 2001, 50(1):141-149.
普氏分析法
[28] bertinetto l, valmadre j, henriques j f, et al. fully-convolutional siamese networks for object tracking[j]. 2016:850-865.
siamese网络
[29] liu x, kan m, wanglong w u, et al. viplfacenet: an open source deep face recognition sdk[j]. frontiers of computer science, 2016, 11(2):208-218.
vipl face ,seetaface引擎的论文
[30] simonyan k, zisserman a. very deep convolutional networks for large-scale image recognition[j]. computer science, 2014.
vggnet的论文，lrn受到质疑
[31] zeiler m d, fergus r. visualizing and understanding convolutional networks[j]. 2013, 8689:818-833.
zfnet论文，用多层连续小卷积核代替单层大卷积核
[32] simon m, rodner e, denzler j. imagenet pre-trained models with batch normalization[j]. 2016.
batch normalization
[33] kingma d p, ba j. adam: a method for stochastic optimization[j]. computer science, 2014.
adam 优化器
[34] landis j r, koch g g. an application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers.[j]. biometrics, 1977, 33(2):363-74.
kappa系数，用于衡量多分类的一致性
[35] bradley a p. the use of the area under the roc curve in the evaluation of machine learning algorithms[m]. elsevier science inc. 1997.
roc 曲线评估机器学习
[36]
lfw 人脸识别排行榜，论文多多
南京复兴号竟在马路上“出轨”撞上公交车？
锤子M1发布，资金紧张销量不佳翻身太难！
三星Galaxy S21系列确认取消附送充电器
MUSES旗舰版新产品『MUSES03』高音质音频运放正式面市
全球首款可堆叠移动电源 电池容量无上限
想了解人脸识别和机器学习？这份论文清单帮大忙
极域：为中小学课堂引入专有“教室云”
物联网的发展规模比你看到的要多得多
增强型GaN HEMT的漏极电流特性
超低功耗Wi-Fi保活门锁方案，让门锁实时在线可控！
基于单片机的系统扩展
区块链在电子商务行业中的应用介绍
基于Syntiant TinyML board的语音助手设计
电容器充放电的原理与注意事项
小米新品笔记本Air发布，升级啊升级，厉害了我的雷军
千元机之战：你选红米Note4X还是360N5？
蓝牙Mesh推动“照明即平台”概念落实
2021智能可穿戴技术创新论坛完美收官，Aigtek高光时刻回顾！
什么是SPEC/Remark?
物联网操作系统将拥抱万亿规模市场
[2]hinton, g.e., osindero, s. and teh, y. (2006) a fast learning algorithm for deep belief nets. neural computation, 18, 1527-1554.
hinton 2006提出的深度学习的概念
[3] krizhevsky a, sutskever i, hinton g e. imagenet classification with deep convolutional neural networks[c]// international conference on neural information processing systems. curran associates inc. 2012:1097-1105.
alexnet的论文
[4] chang f, dean j, ghemawat s, et al. bigtable: a distributed storage system for structured data[j]. acm transactions on computer systems, 2008, 26(2):1-26.
[5] dean j, ghemawat s. mapreduce: simplified data processing on large clusters[m]. acm, 2008.
[6] ghemawat s, gobioff h, leung s. file and storage systems: the google file system[j]. acm symposium on operating systems principles bolton landing, 2003, 37:29-43.
google大数据三论文
[7] yang j, lu w, waibel a. skin-color modeling and adaptation[j]. technical report, 1997, 1352:687-694.
[8] moghaddam b, pentland a. probabilistic visual learning for object representation[m]. ieee computer society, 1997.
基于肤色的人脸分割方法
[9] garcia c, tziritas g. face detection using quantized skin color regions merging and wavelet packet analysis[j]. ieee transactions on multimedia, 2002, 1(3):264-277.
先验知识模型
[10] yang j, zhang d, frangi a f, et al. two-dimensional pca: a new approach to appearance-based face representation and recognition[j]. ieee trans pattern anal mach intell, 2004, 26(1):131-137.
pca脸
[11] goldberg p, jerrum m. bounding the vapnik-chervonenkis dimension of concept classes parameterized by real numbers[c]// conference on computational learning theory. acm, 1993:361-369.
svm算法，软间隔最大化
[12] huang j, blanz v, heisele b. face recognition using component-based svm classification and morphable models[c]// international workshop on pattern recognition with support vector machines. springer-verlag, 2002:334-341.
svm与模板，识别人脸
[13] wang x. an hog-lbp human detector with partial occlusion handling[j]. proc.ieee int.conf.on computer vision kyoto japan sept, 2009, 30(2):32-39.
hog 人脸 ,lbp 人脸特征抽取
[14] mita t, kaneko t, hori o. joint haar-like features for face detection[c]// tenth ieee international conference on computer vision. 2005:1619-1626.
haar 特征，一种小波变换
[14] drucker h, cortes c, jackel l d, et al. boosting and other ensemble methods[j]. neural computation, 1989, 6(6):1289-1301.
yann lecun cnn
[15] lecun y, bengio y. convolutional networks for images, speech, and time series[m]// the handbook of brain theory and neural networks. mit press, 1998.
lecun y, bengio y. 于1998年提出的卷积神经网络的一些应用
[16] hubel d h, wiesel t n. receptive fields, binocular interaction and functional architecture in the cat’s visual cortex[j]. journal of physiology, 1962, 160(1):106-154.
卷积神经网络诞生的灵感来源，研究猫的视觉神经
[17] lecun y. lenet-5, convolutional neural networks[j].
lenet
[18] taigman y, yang m, ranzato m, et al. deepface: closing the gap to human-level performance in face verification[c]// ieee conference on computer vision and pattern recognition. ieee computer society, 2014:1701-1708.
deepface，经典的人脸识别论文，网上有大量该论文笔记
[19] he k, zhang x, ren s, et al. deep residual learning for image recognition[j]. 2015:770-778.
resnet ,state-of-the-art
[20] dechter r. learning while searching in constraint-satisfaction-problems[c]// national conference on artificial intelligence. philadelphia, pa, august 11-15, 1986. volume 1: science. dblp, 1986:178-185.
最早的深度学习概念，第一次提出的深度学习说法
[21] viola p, jones m. rapid object detection using a boosted cascade of simple features[c]// computer vision and pattern recognition, 2001. cvpr 2001. proceedings of the 2001 ieee computer society conference on. ieee, 2003:i-511-i-518 vol.1.
haar 级联人脸检测方法
[22] viola p, jones m j. robust real-time face detection[c]// computer vision, 2001. iccv 2001. proceedings. eighth ieee international conference on. ieee xplore, 2004:747.
部件人脸检测，经典论文
[23] girshick r. fast r-cnn[j]. computer science, 2015.
fast r-cnn
[24] ren s, he k, girshick r, et al. faster r-cnn: towards real-time object detection with region proposal networks.[j]. ieee transactions on pattern analysis & machine intelligence, 2015, 39(6):1137-1149.
faster-rcnn
[25] deep dense face detector spots faces from a wide range of angles[j]. biometric technology today, 2015, 2015:12.
yahoo ,ddfd，一种人脸检测方法
[26] zhang k, zhang z, li z, et al. joint face detection and alignment using multitask cascaded convolutional networks[j]. ieee signal processing letters, 2016, 23(10):1499-1503.
mtcnn，state-of-the-art 人脸检测方法
[27] slice d e. landmark coordinates aligned by procrustes analysis do not lie in kendall’s shape space.[j]. systematic biology, 2001, 50(1):141-149.
普氏分析法
[28] bertinetto l, valmadre j, henriques j f, et al. fully-convolutional siamese networks for object tracking[j]. 2016:850-865.
siamese网络
[29] liu x, kan m, wanglong w u, et al. viplfacenet: an open source deep face recognition sdk[j]. frontiers of computer science, 2016, 11(2):208-218.
vipl face ,seetaface引擎的论文
[30] simonyan k, zisserman a. very deep convolutional networks for large-scale image recognition[j]. computer science, 2014.
vggnet的论文，lrn受到质疑
[31] zeiler m d, fergus r. visualizing and understanding convolutional networks[j]. 2013, 8689:818-833.
zfnet论文，用多层连续小卷积核代替单层大卷积核
[32] simon m, rodner e, denzler j. imagenet pre-trained models with batch normalization[j]. 2016.
batch normalization
[33] kingma d p, ba j. adam: a method for stochastic optimization[j]. computer science, 2014.
adam 优化器
[34] landis j r, koch g g. an application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers.[j]. biometrics, 1977, 33(2):363-74.
kappa系数，用于衡量多分类的一致性
[35] bradley a p. the use of the area under the roc curve in the evaluation of machine learning algorithms[m]. elsevier science inc. 1997.
roc 曲线评估机器学习
[36]
lfw 人脸识别排行榜，论文多多
南京复兴号竟在马路上“出轨”撞上公交车？
锤子M1发布，资金紧张销量不佳翻身太难！
三星Galaxy S21系列确认取消附送充电器
MUSES旗舰版新产品『MUSES03』高音质音频运放正式面市
全球首款可堆叠移动电源 电池容量无上限
想了解人脸识别和机器学习？这份论文清单帮大忙
极域：为中小学课堂引入专有“教室云”
物联网的发展规模比你看到的要多得多
增强型GaN HEMT的漏极电流特性
超低功耗Wi-Fi保活门锁方案，让门锁实时在线可控！
基于单片机的系统扩展
区块链在电子商务行业中的应用介绍
基于Syntiant TinyML board的语音助手设计
电容器充放电的原理与注意事项
小米新品笔记本Air发布，升级啊升级，厉害了我的雷军
千元机之战：你选红米Note4X还是360N5？
蓝牙Mesh推动“照明即平台”概念落实
2021智能可穿戴技术创新论坛完美收官，Aigtek高光时刻回顾！
什么是SPEC/Remark?
物联网操作系统将拥抱万亿规模市场