Biometric Recognition

Accuracy and efficiency are two conflicting challenges for face detection, since effective models tend to be computationally prohibitive. To address these two conflicting challenges, our core idea is to shrink the input image and focus on detecting small faces. Specifically, we propose a novel face detector, dubbed the name Densely Connected Face Proposal Network (DCFPN), with high performance as well as real-time speed on the CPU devices. On the one hand, we subtly design a lightweight-butpowerful fully convolutional network with the consideration of efficiency and accuracy. On the other hand, we use the dense anchor strategy and propose a fair L1 loss function to handle small faces well. As a consequence, our method can detect faces at 30 FPS on a single 2.60 GHz CPU core and 250 FPS using a GPU for the VGA-resolution images. We achieve state-of-the-art performance on the AFW, PASCAL face and FDDB datasets.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  David Zhang,et al.  Competitive coding scheme for palmprint verification , 2004, ICPR 2004.

[3]  Zhenhua Guo,et al.  The multiscale competitive code via sparse representation for palmprint verification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.

[5]  Jun Wang,et al.  Self-taught hashing for fast similarity search , 2010, SIGIR.

[6]  M. Swerts,et al.  Verbal and Nonverbal Correlates for Depression: A Review , 2012 .

[7]  I. Jones,et al.  Some Nonverbal Aspects of Depression and Schizophrenia Occurring during the Interview , 1979, The Journal of nervous and mental disease.

[8]  Victor S. Lempitsky,et al.  Aggregating Local Deep Features for Image Retrieval , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[9]  Renjie Liao,et al.  Deep Edge-Aware Filters , 2015, ICML.

[10]  John Shawe-Taylor,et al.  Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

[11]  David Zhang,et al.  Two novel characteristics in palmprint verification: datum point invariance and line feature matching , 1999, Pattern Recognit..

[12]  David Zhang,et al.  Half-orientation extraction of palmprint features , 2016, Pattern Recognit. Lett..

[13]  David Suter,et al.  A General Two-Step Approach to Learning-Based Hashing , 2013, 2013 IEEE International Conference on Computer Vision.

[14]  K. KRISHNESWARI,et al.  INTRAMODAL FEATURE FUSION USING WAVELET FOR PALMPRINT AUTHENTICATION , 2011 .

[15]  A. B. Negrão,et al.  Major Depressive Disorder , 2007 .

[16]  David Zhang,et al.  Multiscale competitive code for efficient palmprint recognition , 2008, 2008 19th International Conference on Pattern Recognition.

[17]  Shaogang Gong,et al.  Facial expression recognition based on Local Binary Patterns: A comprehensive study , 2009, Image Vis. Comput..

[18]  David Zhang,et al.  Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition , 2016, Pattern Recognit..

[19]  Tieniu Tan,et al.  Ordinal palmprint represention for personal identification [represention read representation] , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[20]  Minyi Guo,et al.  Supervised hashing with latent factor models , 2014, SIGIR.

[21]  Anil K. Jain,et al.  Latent Palmprint Matching , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Dacheng Tao,et al.  Transform-Invariant Convolutional Neural Networks for Image Classification and Search , 2016, ACM Multimedia.

[23]  Jeffrey F. Cohn,et al.  Detecting Depression Severity from Vocal Prosody , 2013, IEEE Transactions on Affective Computing.

[24]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[25]  Zhenhua Guo,et al.  Palmprint verification using binary orientation co-occurrence vector , 2009, Pattern Recognit. Lett..

[26]  D. Basak,et al.  Support Vector Regression , 2008 .

[27]  Olga V. Demler,et al.  The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). , 2003, JAMA.

[28]  Michael Wagner,et al.  Multimodal assistive technologies for depression diagnosis and monitoring , 2013, Journal on Multimodal User Interfaces.

[29]  Hanjiang Lai,et al.  Supervised Hashing for Image Retrieval via Image Representation Learning , 2014, AAAI.

[30]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Jiwen Lu,et al.  Deep hashing for compact binary codes learning , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Xin Li,et al.  Automated Depression Diagnosis Based on Facial Dynamic Analysis and Sparse Coding , 2015, IEEE Transactions on Information Forensics and Security.

[33]  David Zhang,et al.  Palmprint recognition using eigenpalms features , 2003, Pattern Recognit. Lett..

[34]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[35]  Zhi-Hua Zhou,et al.  Column Sampling Based Discrete Supervised Hashing , 2016, AAAI.

[36]  Guodong Guo,et al.  Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics , 2018, IEEE Transactions on Affective Computing.

[37]  H A Pincus,et al.  The societal costs of chronic major depression. , 2001, The Journal of clinical psychiatry.

[38]  H P Hirsbrunner,et al.  Analyzing nonverbal behavior in depression. , 1983, Journal of abnormal psychology.

[39]  David Zhang,et al.  Online Palmprint Identification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..