Identyfikatory
DOI
Warianty tytułu
Języki publikacji
Abstrakty
Development of facial recognition or expression recognition algorithms requires input data to thoroughly test the performance of algorithms in various conditions. Researchers are developing various methods to face challenges like illumination, pose and expression changes, as well as facial disguises. In this paper, we propose and establish a dataset of thermal facial images, which contains a set of neutral images in various poses as well as a set of facial images with different posed expressions collected with a thermal infrared camera. Since the properties of face in the thermal domain strongly depend on time, in order to show the impact of aging, collection of the dataset has been repeated and a corresponding set of data is provided. The paper describes the measurement methodology and database structure. We present baseline results of processing using state-of-the-art facial descriptors combined with distance metrics for thermal face re-identification. Three selected local descriptors, a histogram of oriented gradients, local binary patterns and local derivative patterns are used for elementary assessment of the database. The dataset offers a wide range of capabilities - from thermal face recognition to thermal expression recognition.
Czasopismo
Rocznik
Tom
Strony
403--415
Opis fizyczny
Bibliogr. 50 poz., rys., tab., wykr.
Twórcy
autor
- Military University of Technology, Institute of Optoelectronics, Gen. W. Urbanowicza 2, 00-908 Warsaw, Poland
autor
- Military University of Technology, Institute of Optoelectronics, Gen. W. Urbanowicza 2, 00-908 Warsaw, Poland
Bibliografia
- [1] Kong, S., Heo, J., Abidi B., Paik, J., Abidi M. (2005). Recent advances in visual and infrared face recognition - a review. Pattern Recognition, 47(9), 2807-2824.
- [2] Abate, A.F., Nappi, M., Riccio, D., Sabatino, G. (2007). 2D and 3D face recognition: A survey. Pattern Recogn. Lett., 28, 1885-1906.
- [3] Adini, Y., Moses, Y., Ullman, S. (1997). Face Recognition: The Problem of Compensating for Changes in Illumination Direction. IEEE Trans. Pattern Anal. Mach. Intell., 19, 721-732.
- [4] Zhang, X., Gao, Y. (2009). Face recognition across pose: A review. Pattern Recogn., 42, 2876-2896.
- [5] Bowyer, K.W., Kyong, C., Flynn, P. (2006). A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition. Comput. Vis. Image Und., 101, 1-15.
- [6] Ghiass, R.Z., Arandjelović, O., Bendada, A., Maldague, X. (2014). Infrared face recognition: A comprehensive review of methodologies and databases. Pattern Recogn., 47, 2807-2824.
- [7] Lettington, A.H., Hong, Q.H. (1993). An objective MRTD for discrete infrared imaging systems. Meas. Sci. Technol., 4, 1106-1110.
- [8] Lahiri, B.B., Bagavathiappan, S., Jayakumar, T., Philip, J. (2012). Medical applications of infrared thermography: A review. Infrared Physics & Technology, 55, 221-235.
- [9] Hackforth, H.L. (1963). Infrared Radiation. WNT, Warsaw.
- [10] Vollmer, M., Mollmannm, K.P. (2010). Infrared thermal imaging: Fundamentals, Research and Applications. Wiley-VCH.
- [11] Costello, J.T., McInerney, C.D., Bleakley, C.M., Selfe, J., Donnelly, A.E. (2012). The use of thermal imaging in assessing skin temperature following cryotherapy: a review. Journal of Thermal Biology, 37, 103-110.
- [12] Equinox Corp. Equinox Human Identification at a Distance Database. http://www.equinoxsensors.com/products/HID.html. (Accessed Mar. 31, 2017).
- [13] Grgic, M., Delac, K., Grgic, S. (2011). SCface - surveillance cameras face database. Multimed. Tools Appl., 51, 863-879.
- [14] Espinosa-Duró, V., Faundez-Zanuy, M., Mekyska, J., Carl Database. Signal Processing Laboratory. http://splab.cz/en/download/databaze/carl-database (2017). (Mar. 31, 2017).
- [15] Espinosa-Duró, V., Faundez-Zanuy, M., Mekyska, J. (2013). A New Face Database Simultaneously Acquired in Visible, Near-Infrared and Thermal Spectrums. Cognitive Computation, 5, 119-135.
- [16] Hammoud, R.I. OTCBVS Benchmark Dataset Collection. http://vcipl-okstate.org/pbvs/bench/. (2017). (Accessed Apr. 10, 2017).
- [17] Wright, D.. Data Sets. Computer Vision Research Laboratory, Department of Computer Science and Engineering, University of Notre Dame. https://sites.google.com/a/nd.edu/public-cvrl/data-sets. (2017) (Accessed Apr. 19, 2017)
- [18] Ghiass, R.S., Arandjelović, O., Bendada, A., Maldague, X. (2013). Illumination-invariant face recognition from a single image across extreme pose using a dual dimension AAM ensemble in the thermal infrared spectrum. The 2013 International Joint Conference on Neural Networks (IJCNN).
- [19] Wang S., et. al. (2010). A Natural Visible and Infrared Facial Expression Database for Expression Recognition and Emotion Inference. IEEE Transactions on Multimedia, 12(7).
- [20] Jackson, J.E. (1991). A User’s Guide to Principal Components. Wiley, New York.
- [21] Yu, H., Yang, J. (2001). A direct LDA algorithm for high-dimensional data - with application to face recognition. Pattern Recogn., 34, 2067-2070.
- [22] Comon, P. (1994). Independent component analysis, A new concept? Signal Process., 36, 287-314.
- [23] Ojala, T., Pietikäinen, M., Harwood, D. (1994). Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. IEEE.
- [24] Ojala, T., Pietikäinen, M., Harwood, D. (1996). A comparative study of texture measures with classification based on feature distributions. Pattern Recogn., 29, 51-59.
- [25] Zou, J., Ji, Q., Nagy, G. (2007). A Comparative Study of Local Matching Approach for Face Recognition. IEEE T. Image Process., 16, 2617-2628.
- [26] Hermosilla, G., Ruiz-del-Solar, J., Verschae, R., Correa, M. (2009). Face Recognition using Thermal Infrared Images for Human-Robot Interaction Applications: A Comparative Study. Robotics Symposium (LARS), 2009 6th Latin American.
- [27] Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., C.hen, X., Gao, W. (2010). WLD: A Robust Local Image Descriptor. IEEE T. Pattern Anal., 32, 1705-1720.
- [28] Bay, H., Ess, A., Tuytelaars, T., Van Gool, L. (2008). SURF: Speeded up Robust Features. Springer, Berlin, Heidelberg.
- [29] Lowe, D.G. (2004). Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vision, 60, 91-110.
- [30] McConnell, R.K. (1986). Method of and apparatus for pattern recognition. US Patent US4567610 A.
- [31] Dalal, N., Triggs, B. (2005). Histograms of oriented gradients for human detection. IEEE Computer Vision and Pattern Recognition.
- [32] Déniz, O., Bueno, G., Salido, J., De la Torre, F. (2011). Face recognition using Histograms of Oriented Gradients. Pattern Recogn. Lett., 32, 1598-1603.
- [33] Calvillo, A.D., Vazquez, R.A., Ambrosio, J., Waltier, A. (2016). Face Recognition Using Histogram Oriented Gradients. Springer.
- [34] Song, F., Tan, X., Liu, X., Chen, S. (2014). Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients. Pattern Recogn., 47, 2825-2838.
- [35] Tan, X., Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE T. Image Process., 19, 1635-1650.
- [36] Ahonen, T., Pietikainen, M. (2007). Soft histograms for local binary patterns. Finnish Signal Processing Symphosium, 1-4.
- [37] Heikkila, M., Pietikainen, M., Schmid, C. (2009). Description of interest regions with local binary patterns. Pattern Recogn., 42, 425-436.
- [38] Zhang, W., Shan, S., Zhang, H., Gao, W., Chen, X. (2005). Multi-resolution histograms of local variation patterns (MHLVP) for robust face recognition. Springer.
- [39] Zhang, W., Shan, S., Zhang, H., Gao, W., Chen, X. (2005). Local gabor binary pattern histogram sequence (LGBPHS): A novel non-statistical model for face representation and recognition. ICCV Tenth IEEE International Conference on Computer Vision.
- [40] Zhang, B., Gao, Y., Zhao, S. (2010). Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor. IEEE T. Image Process., 19, 533-544.
- [41] Deza, E., Deza, M.M. (2006). Dictionary of Distances. Elsevier.
- [42] Yin, S., Dai, X., Ouyang, P., Liu, L., Wei, S. (2014). A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps. Sensors, 14, 19561-19581.
- [43] Ren, H., Sun, J., Hao, Y., Yan, X., Liu, X. (2014). Uniform Local Derivative Patterns and Their Application in Face Recognition. Journal of Signal Processing Systems, 74, 405-416.
- [44] Zhou, H.X., Lai, R., Liu, S.Q., Jiang, G. (2005). New improved nonuniformity correction for infrared focal plane arrays. Optics Communications, 245, 49-53.
- [45] Lim, J.S., (1990). Two-Dimensional Signal and Image Processing. Englewood Cliffs, NJ, Prentice Hall.
- [46] Jones, P.V.J. (2004). Robust Real-Time Face Detection. International Journal of Computer Vision.
- [47] False Match Rate. Computational Methods in Biometric Authentication. Information Science and Statistics. Springer, London.
- [48] Schuckers, M.E. (2010). False Non-Match Rate. Computational Methods in Biometric Authentication. Information Science and Statistics. Springer, London.
- [49] Tan, P.N. (2009). Receiver Operating Characteristic. Encyclopedia of Database Systems. Springer, Boston, MA.
- [50] WAT thermal face database. http://safe.wat.edu.pl/?page_id=17. (Accessed Feb. 13, 2018).
Uwagi
EN
1. This project has been funded by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 700259. The anonymized WAT thermal face dataset is available online on request [50]. The authors would like to thank all the subjects who participated in the experiments.
PL
2. Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d00dadcc-8eaf-4883-9b6e-a1a66fd44ba6