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11K Hands: Gender recognition and biometric identification using a large dataset of hand images


Human hand not only possesses distinctive feature for gender information, it is also considered one of the primary biometric traits used to identify a person. Unlike face images, which are usually unconstrained, an advantage of hand images is they are usually captured under a controlled position. Most state-of-the-art methods, that rely on hand images for gender recognition or biometric identification, employ handcrafted features to train an off-the-shelf classifier or be used by a similarity metric for biometric identification. In this work, we propose a deep learning-based method to tackle the gender recognition and biometric identification problems. Specifically, we design a two-stream convolutional neural network (CNN) which accepts hand images as input and predicts gender information from these hand images. This trained model is then used as a feature extractor to feed a set of support vector machine classifiers for biometric identification. As part of this effort, we propose a large dataset of human hand images, 11K Hands, which contains dorsal and palmar sides of human hand images with detailed ground-truth information for different problems including gender recognition and biometric identification. By leveraging thousands of hand images, we could effectively train our CNN-based model achieving promising results. One of our findings is that the dorsal side of human hands is found to have effective distinctive features similar to, if not better than, those available in the palmar side of human hand images. To facilitate access to our 11K Hands dataset, the dataset, the trained CNN models, and our Matlab source code are available at (

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  1. Abdel-Hakim A E, Farag A A (2006) Csift: A sift descriptor with color invariant characteristics. In: IEEE conference on computer vision and pattern recognition, vol 2, pp 1978–1983

  2. Afifi M, Abdelhamed A (2017) Afif4: Deep gender classification based on adaboost-based fusion of isolated facial features and foggy faces. arXiv:170604277

  3. Afifi M, Nasser M, Korashy M, Rohde K, Mohamed AA (2018) Can we boost the power of the viola–jones face detector using preprocessing? an empirical study. J Electron Imaging 27(4):043020–1 – 043020–14

    Article  Google Scholar 

  4. Amayeh G, Bebis G, Nicolescu M (2008) Gender classification from hand shape. In: IEEE conference on computer vision and pattern recognition workshops, pp 1–7

  5. Angadi S, Hatture S (2018) Hand geometry based user identification using minimal edge connected hand image graph. IET Computer Vision

  6. Aslam A, Hussain B, Cetin AE, Umar AI, Ansari R (2018) Gender classification based on isolated facial features and foggy faces using jointly trained deep convolutional neural network. J Electron Imaging 27(5):053023–1–053023–6

    Article  Google Scholar 

  7. Azizpour H, Razavian A S, Sullivan J, Maki A, Carlsson S (2016) Factors of transferability for a generic convnet representation. IEEE Trans Pattern Anal Mach Intell 38(9):1790–1802

    Article  Google Scholar 

  8. Bera A, Bhattacharjee D (2017) Human identification using selected features from finger geometric profiles. IEEE Trans Syst Man Cybern Syst PP(99):1–15

    Article  Google Scholar 

  9. Bera A, Bhattacharjee D, Nasipuri M (2017) Finger contour profile based hand biometric recognition. Multimed Tools Appl 76(20):21451–21479

    Article  Google Scholar 

  10. Bilinski P, Dantcheva A, Brémond F (2016) Can a smile reveal your gender? In: International conference of the biometrics special interest group, pp 1–6

  11. Charfi N, Trichili H, Alimi AM, Solaiman B (2014) Novel hand biometric system using invariant descriptors. In: 6th international conference of soft computing and pattern recognition, pp 261–266

  12. Charfi N, Trichili H, Alimi A M, Solaiman B (2017) Bimodal biometric system for hand shape and palmprint recognition based on sift sparse representation. Multimed Tools Appl 76(20):20457–20482

    Article  Google Scholar 

  13. Chatfield K, Simonyan K, Vedaldi A, Zisserman A (2014) Return of the devil in the details: delving deep into convolutional nets. In: British machine vision conference

  14. Chen C, Dantcheva A, Ross A (2014) Impact of facial cosmetics on automatic gender and age estimation algorithms. In: 2014 international conference on computer vision theory and applications (VISAPP), IEEE, vol 2, pp 182–190

  15. Conaire CO, O’Connor NE, Smeaton AF (2007) Detector adaptation by maximising agreement between independent data sources. In: IEEE conference on computer vision and pattern recognition, pp 1–6

  16. Csurka G, Dance C, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision, European conference on computer vision, vol 1, pp 1–2

  17. Dantcheva A, Elia P, Ross A (2016) What else does your biometric data reveal? A survey on soft biometrics. IEEE Trans Inf Forensics Secur 11(3):441–467

    Article  Google Scholar 

  18. Farbman Z, Fattal R, Lischinski D, Szeliski R (2008) Edge-preserving decompositions for multi-scale tone and detail manipulation. In: ACM Transactions on Graphics, vol 27, p 67

  19. Ferrer MA, Morales A, Travieso CM, Alonso JB (2007) Low cost multimodal biometric identification system based on hand geometry, palm and finger print texture. In: 41st annual IEEE international carnahan conference on security technology, pp 52–58

  20. Guebla A, Meraoumia A, Bendjenna H, Chitroub S (2016) Using of finger-knuckle-print in biometric security systems. In: 2016 international conference on information technology for organizations development (IT4OD), pp 1–5

  21. Gurnani A, Gajjar V, Mavani V, Khandhediya Y (2018) Vegac: Visual saliency-based age, gender, and facial expression classification using convolutional neural networks. arXiv:180305719

  22. Han C C (2004) A hand-based personal authentication using a coarse-to-fine strategy. Image Vis Comput 22(11):909–918

    Article  Google Scholar 

  23. He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409

    Article  Google Scholar 

  24. Hu R X, Jia W, Zhang D, Gui J, Song L T (2012) Hand shape recognition based on coherent distance shape contexts. Pattern Recogn 45(9):3348–3359

    Article  Google Scholar 

  25. Jain A K, Dass S C, Nandakumar K (2004) Can soft biometric traits assist user recognition?. In: Biometric technology for human identification, vol 5404, pp 561–573

  26. Kanchan T, Krishan K (2011) Anthropometry of hand in sex determination of dismembered remains-a review of literature. J Forensic Leg Med 18(1):14–17

    Article  Google Scholar 

  27. Kecman V, Huang T M, Vogt M (2005) Iterative single data algorithm for training kernel machines from huge data sets: theory and performance, Support vector machines: Theory and Applications, 605–605

  28. Kong A, Zhang D, Kamel M (2009) A survey of palmprint recognition. Pattern Recogn 42(7):1408–1418

    Article  Google Scholar 

  29. Krishan K, Kanchan T, Sharma A (2011) Sex determination from hand and foot dimensions in a north indian population. J Forensic Sci 56(2):453–459

    Article  Google Scholar 

  30. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  31. Kuehlkamp A, Becker B, Bowyer K (2017) Gender-from-iris or gender-from-mascara?. In: 2017 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 1151–1159

  32. Kumar A (2008) Incorporating cohort information for reliable palmprint authentication. In: 6th Indian conference on computer vision, graphics & image processing, pp 583–590

  33. Kumar A, Shekhar S (2010) Palmprint recognition using rank level fusion. In: 17th IEEE international conference on image processing, pp 3121–3124

  34. Lagree S, Bowyer KW (2011) Predicting ethnicity and gender from iris texture. In: IEEE international conference on technologies for homeland security, pp 440–445

  35. Lapuschkin S, Binder A, Müller KR, Samek W (2017) Understanding and comparing deep neural networks for age and gender classification. In: IEEE conference on computer vision and pattern recognition, pp 1629–1638

  36. Li X, Zhao X, Fu Y, Liu Y (2010) Bimodal gender recognition from face and fingerprint. In: IEEE conference on computer vision and pattern recognition, pp 2590–2597

  37. Li Y, Zeng J, Shan S, Chen X (2018) Occlusion aware facial expression recognition using cnn with attention mechanism. IEEE Transactions on Image Processing

  38. McFadden D, Shubel E (2002) Relative lengths of fingers and toes in human males and females. Horm Behav 42(4):492–500

    Article  Google Scholar 

  39. Mordvintsev A, Olah C, Tyka M (2015) Deep dream

  40. Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press

  41. Nanni L, Ghidoni S, Brahnam S (2017) Handcrafted vs non-handcrafted features for computer vision classification. Pattern Recognition

  42. Nixon KA, Aimale V, Rowe R (2008) Handbook of biometrics. Springer, New York

    Google Scholar 

  43. Nixon M S, Correia P L, Nasrollahi K, Moeslund T B, Hadid A, Tistarelli M (2015) On soft biometrics. Pattern Recogn Lett 68:218–230

    Article  Google Scholar 

  44. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  45. Omar R R, Han T, Al-Sumaidaee SA, Chen T (2018) Deep finger texture learning for verifying people. IET Control Theory and Applications

  46. Petschnigg G, Szeliski R, Agrawala M, Cohen M, Hoppe H, Toyama K (2004) Digital photography with flash and no-flash image pairs. ACM Trans Graph 23(3):664–672

    Article  Google Scholar 

  47. Ranjan R, Patel V M, Chellappa R (2019) Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Trans Pattern Anal Mach Intell 41(1):121–135

    Article  Google Scholar 

  48. Sánchez J, Perronnin F, Mensink T, Verbeek J (2013) Image classification with the fisher vector: theory and practice. Int J Comput Vis 105(3):222–245

    MathSciNet  Article  MATH  Google Scholar 

  49. Shanmugasundaram K, Mohamed ASA, Ruhaiyem NIR (2017) An overview of hand-based multimodal biometrie system using multi-classifier score fusion with score normalization. In: 2017 international conference on signal processing and communication (ICSPC), pp 53–57

  50. Sharma S, Dubey S R, Singh S K, Saxena R, Singh R K (2015) Identity verification using shape and geometry of human hands. Expert Syst Appl 42(2):821–832

    Article  Google Scholar 

  51. Sun Y, Zhang M, Sun Z, Tan T (2018) Demographic analysis from biometric data: Achievements, challenges, and new frontiers. IEEE Trans Pattern Anal Mach Intell 40(2):332–351

    Article  Google Scholar 

  52. Sun Z, Tan T, Wang Y, Li S Z (2005) Ordinal palmprint represention for personal identification. In: IEEE conference on computer vision and pattern recognition, vol 1, pp 279–284

  53. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE conference on computer vision and pattern recognition, pp 1–9

  54. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: 6th international conference on computer vision, pp 839–846

  55. Unar J, Seng W C, Abbasi A (2014) A review of biometric technology along with trends and prospects. Pattern Recogn 47(8):2673–2688

    Article  Google Scholar 

  56. Van De Sande K, Gevers T, Snoek C (2010) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Anal Mach Intell 32(9):1582–1596

  57. Wang Z, Bovik A C, Sheikh H R, Simoncelli E P (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13 (4):600–612

    Article  Google Scholar 

  58. Wu M, Yuan Y (2014) Gender classification based on geometry features of palm image. The Scientific World Journal 2014

  59. Xia B (2018) Which facial expressions can reveal your gender? A study with 3d faces. arXiv:180500371

  60. Xie J, Zhang L, You J, Zhang D, Qu X (2012) A study of hand back skin texture patterns for personal identification and gender classification. Sensors 12 (7):8691–8709

    Article  Google Scholar 

  61. Yoruk E, Konukoglu E, Sankur B, Darbon J (2006) Shape-based hand recognition. IEEE Trans Image Process 15(7):1803–1815

    Article  Google Scholar 

  62. Yu X, Huang J, Zhang S, Metaxas D N (2016) Face landmark fitting via optimized part mixtures and cascaded deformable model. IEEE Trans Pattern Anal Mach Intell 38(11):2212–2226

    Article  Google Scholar 

  63. Zhang D D, Kong W, You J, Wong M (2003) Online palmprint identification. IEEE Transactions on pattern analysis and machine intelligence

  64. Zhang L, Li L, Yang A, Shen Y, Yang M (2017) Towards contactless palmprint recognition: A novel device, a new benchmark, and a collaborative representation based identification approach. Pattern Recogn 69:199–212

    Article  Google Scholar 

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Correspondence to Mahmoud Afifi.

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Afifi, M. 11K Hands: Gender recognition and biometric identification using a large dataset of hand images. Multimed Tools Appl 78, 20835–20854 (2019).

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  • Gender recognition
  • Gender classification
  • Biometric identification
  • Deep learning
  • CNN
  • Hands dataset