Abstract
This article focuses on identifying tiny faces in thermal images using transfer learning. Although the issue of identifying faces in images is not new, the problem of tiny face identification is a recently identified research area. Indeed challenging, however, in this paper, we take the problem one step ahead and focus on recognizing tiny faces in thermal images. To do that, we use the paradigm of transfer learning. We use the famous residual network to extract the features in the target domain. Subsequently, with this model as a reference point, we then retrain it in the target domain of thermal images. Through testing performed in Terravic datasets, we have found that the method outperforms existing methods in literature to identify tiny faces in thermal images.






Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.Change history
02 December 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-024-04919-3
References
Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 12:2037–2041
Azab AM, Toth J, Mihaylova LS, Arvaneh M (2018) A review on transfer learning approaches in brain–computer interface. In: Signal processing and machine learning for brain–machine interfaces
Bai Y, Zhang Y, Ding M, Ghanem B (2018) Finding tiny faces in the wild with generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 21–30
Cevikalp H, Neamtu M, Wilkes M, Barkana A (2005) Discriminative common vectors for face recognition. IEEE Trans Pattern Anal Mach Intell 27(1):4–13
Cheah S, Hussin R, Kamarudin A, Mohyar S, Taking S, Aziz M, Kasjoo S (2018) Human trapped in a parked car recognition using thermal image approach. In: AIP conference proceedings, vol 2045. AIP, p 020086
Dai W, Yang Q, Xue G, Yu Y (2007) Boosting for transfer learning. In: Machine learning, proceedings of the twenty-fourth international conference (ICML 2007), Corvallis, Oregon, USA, June 20–24, 2007. pp 193–200
Dong C, Loy CC, He K, Tang X (2016) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2):295–307
Farfade SS, Saberian MJ, Li L-J (2015) Multi-view face detection using deep convolutional neural networks. In: Proceedings of the 5th ACM on international conference on multimedia retrieval. ACM, pp 643–650
Gaber T, Tharwat A, Ibrahim A, Snáel V, Hassanien AE (2015) Human thermal face recognition based on random linear oracle (rlo) ensembles. In: 2015 international conference on intelligent networking and collaborative systems. IEEE, pp 91–98
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680
Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertain Fuzziness Knowl Based Syst 6(02):107–116
Hu G, Yang Y, Yi D, Kittler J, Christmas W, Li SZ, Hospedales T (2015) When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition. In: Proceedings of the IEEE international conference on computer vision workshops, pp 142–150
Hu P, Ramanan D (2017) Finding tiny faces. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 951–959
Ibrahim A, Tharwat A, Gaber T, Hassanien AE (2018) Optimized superpixel and adaboost classifier for human thermal face recognition. Signal Image Video Process 12(4):711–719
Kim D, Yun W-h, Lee J (2010) Tiny frontal face detection for robots. In: 2010 3rd international conference on human-centric computing, IEEE, pp 1–4
Kim J, Kwon Lee J, Mu Lee K (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1646–1654
Kirby M, Sirovich L (1990) Application of the karhunen-loeve procedure for the characterization of human faces. IEEE Trans Pattern Anal Mach Intell 12(1):103–108
Kuhlmann G, Stone P, (2007) Graph-based domain mapping for transfer learning in general games. In: European conference on machine learning. Springer, pp 188–200
Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 8(1):98–113
Li B, Yang Q, Xue X (2009a) Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction. In: Twenty-First international joint conference on artificial intelligence
Li B, Yang Q, Xue X (2009b) Transfer learning for collaborative filtering via a rating-matrix generative model. In: Proceedings of the 26th annual international conference on machine learning. ACM, pp 617–624
Li C, Zhao S, Song W, Xiao K, Wang Y (2017) Ubiquitous single-sample face recognition under occlusion based on sparse representation with dual features. J Ambient Intell Hum Comput 1–11
Liu X-Z, Ye H-W (2015) Dual-kernel based 2d linear discriminant analysis for face recognition. J Ambient Intell Hum Comput 6(5):557–562
Lu G, Yan Y, Ren L, Saponaro P, Sebe N, Kambhamettu C (2016) Where am i in the dark: exploring active transfer learning on the use of indoor localization based on thermal imaging. Neurocomputing 173:83–92
Lu J, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using lda-based algorithms. IEEE Trans Neural Netw 14(1):195–200
Martínez AM, Kak AC (2001) Pca versus lda. IEEE Trans Pattern Anal Mach Intell 23(2):228–233
Miezianko R (2005) Terravic research infrared database. IEEE OTCBVS WS Series Bench
Pan SJ, Kwok JT, Yang Q (2008a) Transfer learning via dimensionality reduction. AAAI 8:677–682
Pan SJ, Kwok JT, Yang Q, Pan JJ (2007) Adaptive localization in a dynamic wifi environment through multi-view learning. In: AAAI, pp 1108–1113
Pan SJ, Shen D, Yang Q, Kwok JT (2008b) Transferring localization models across space. In: AAAI, pp 1383–1388
Pan SJ, Yang Q (2010a) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359
Pan SJ, Yang Q (2010b) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359
Parkhi OM, Vedaldi A, Zisserman A, et al (2015) Deep face recognition. In: bmvc. Vol. 1. p. 6
Ranjan R, Patel VM, 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
Ranjan R, Sankaranarayanan S, Castillo CD, Chellappa R (2017) An all-in-one convolutional neural network for face analysis. In: 2017 12th IEEE international conference on automatic face & gesture recognition (FG 2017). IEEE, pp 17–24
Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815–823
Seal A, Ganguly S, Bhattacharjee D, Nasipuri M, Basu DK, (2013) Thermal human face recognition based on haar wavelet transform and series matching technique. In: Multimedia processing, communication and computing applications. Springer, pp 155–167
Shi X, Fan W, Ren J (2008) Actively transfer domain knowledge. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, pp 342–357
Singh R, Om H (2013) An overview of face recognition in an unconstrained environment. In: 2013 IEEE second international conference on image information processing (ICIIP-2013). IEEE, pp 672–677
Singh R, Om H (2016a) Illumination invariant face recognition of newborn using single gallery image. Proc Natl Acad Sci India Sect A Phys Sci 86(3):371–376
Singh R, Om H (2016b) Pose invariant face recognition for new born: machine learning approach. In: Computational intelligence in data mining–volume 1. Springer, pp 29–37
Singh R, Om H (2017a) Newborn face recognition using deep convolutional neural network. Multimedia Tools Appl 76(18):19005–19015
Singh R, Om H (2017b) (two-dimensional) 2 whitening reconstruction for newborn recognition. Multimedia Tools Appl 76(3):3471–3483
Sun Y, Chen Y, Wang X, Tang X (2014a) Deep learning face representation by joint identification-verification. In: Advances in neural information processing systems, pp 1988–1996
Sun Y, Liang D, Wang X, Tang X (2015) Deepid3: Face recognition with very deep neural networks. arXiv preprint arXiv:1502.00873
Sun Y, Wang X, Tang X (2014b) Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1891–1898
Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-First AAAI conference on artificial intelligence
Taylor ME, Stone P (2009) Transfer learning for reinforcement learning domains: a survey. J Mach Learn Res 10:1633–1685
Thrun S, Pratt L (2012) Learning to learn. Springer, Berlin
VenkateswarLal P, Nitta GR, Prasad A (2019) Ensemble of texture and shape descriptors using support vector machine classification for face recognition. J Ambient Intell Hum Comput 1–8
Wen Y, Zhang K, Li Z, Qiao Y (2016) A discriminative feature learning approach for deep face recognition. In: European conference on computer vision. Springer, pp 499–515
Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227
Wu Y, Ji Q (2016) Constrained deep transfer feature learning and its applications. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5101–5109
Wu Z, Yuan J, Zhang J, Huang H (2016) A hierarchical face recognition algorithm based on humanoid nonlinear least-squares computation. J Ambient Intell Hum Comput 7(2):229–238
Xu X, Sun D, Pan J, Zhang Y, Pfister H, Yang M-H (2017)Learning to super-resolve blurry face and text images. In: Proceedings of the IEEE international conference on computer vision, pp 251–260
Yang N, Yuan M, Wang P, Zhang R, Sun J, Mao H (2019) Tea diseases detection based on fast infrared thermal image processing technology. J Sci Food Agric 99(7):3459–3466
Ye M, Lan X, Li J, Yuen PC (2018) Hierarchical discriminative learning for visible thermal person re-identification. In: Thirty-Second AAAI conference on artificial intelligence
Yin J, Yang Q, Ni L (2005) Adaptive temporal radio maps for indoor location estimation. In: null. IEEE, pp 85–94
Yu X, Porikli F (2017) Face hallucination with tiny unaligned images by transformative discriminative neural networks. In: Thirty-First AAAI conference on artificial intelligence
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-024-04919-3"
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Singh, R., Ahmed, T., Singh, R. et al. RETRACTED ARTICLE: Identifying tiny faces in thermal images using transfer learning. J Ambient Intell Human Comput 11, 1957–1966 (2020). https://doi.org/10.1007/s12652-019-01470-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-019-01470-4