Abstract
With the development of AR and VR, depth images are widely used for facial expression analysis and recognition. To reduce the storage size and save bandwidth, an efficient compression framework is desired. In this paper, we propose a novel lossless compression framework for facial depth images in expression recognition. In the proposed framework, two steps are designed to remove the redundancy in the facial depth images, which are data preparing and bitstream encoding operations. In the data preparing operation, the original image is represented by the same and different parts between the left and right sides. In the bitstream encoding operation, these parts are compressed to get the final bitstream. The proposed framework is implemented and examined on the BU-3DFE Database. Experimental result shows that the proposed technique outperforms existing lossless compression frameworks in terms of compression efficiency, and the average data size is reduced to 25.27% by the proposed framework.
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References
Bellard F (2018) The BPG image format. [online] Available: http://bellard.org/bpg/
Bossen F, Bross B, Suhring K, Flynn D (2012) HEVC Complexity and implementation analysis. IEEE Trans Circuits Syst Video Technol 22 (12):1685–1696
Chatzikyriakidis E, Papaioannidis C, Pitas I (2019) Adversarial face de-identification. 2019 IEEE International conference on image processing (ICIP), pp 684–688
Colombo A, Cusano C, Schettini R (2006) 3D Face detection using curvature analysis. Pattern Recognit 39(3):444–455
Ding C, Xu C, Tao D (2015) Multi-task pose-invariant face recognition. IEEE Trans Image Process 24(3):980–993
Elaiwat S, Bennamoun M, Boussaid F, El-Sallam A (2014) 3-D Face recognition using curvelet local features. IEEE Signal Process Lett 21 (2):172–175
Eze A, Michael M, Ali B (2018) Lossless image compression using reversible integer wavelet transforms and convolutional neural networks. 2018 data compression conference
Furht B. (ed) (2008) Portable Network Graphics (PNG). In: Encyclopedia of multimedia. Springer, Boston
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, 2016 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 770–778
Kang S, Lee J, Kim C, Yoo H (2018) B-face: 0.2 MW CNN-based face recognition processor with face alignment for mobile user identification. 2018 IEEE symposium on VLSI circuits, pp 137–138
Knuth D (1985) Dynamic Huffman coding. J Algorithms 6:163–180
Lin J (2020) Reversible integer-to-integer wavelet filter design for lossless image compression. IEEE Access, pp 89117–89129
Marcellin MW, Gormish MJ, Bilgin A, et al. (2000) An overview of JPEG-2000. Data compression conference, pp 523–541
Mentzer F, Agustsson E, Tschannen M, et al. (2019) Practical full resolution learned lossless image compression. 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR)
Oliver G, Dario A, Stanco F, et al. (2018) A fast palette reordering technique based on GPU-optimized genetic algorithms, pp 1138–1142
Oswal S, Singh A, Kumari K (2016) Deflate compression algorithm. Int J Eng Res Gen Sci 2016 4(1):430–436
Sab AB, Eswaran P, Kumar C (2016) Lossless compression algorithm using improved RLC for grayscale image. Arabian J Sci Eng
Schiopu I, Munteanu A (2020) Deep-learning-based lossless image coding. IEEE Transactions on Circuits Sys Vid Technol 30(7):1829–1842
Si Z, Shen K (2016) Research on the WebP image format. Advanced graphic communications, packaging technology and materials, pp 271–277
Sullivan GJ, Ohm JR, Han WJ, Wiegand T (2012) Overview of the high efficiency video coding (HEVC) standard. IEEE Trans Circuits Syst Video Technol 22(12):1649–1668
Szegedy C, Liu W, Jia Y, et al. (2014) Going deeper with convolutions. 2014 IEEE/CVF conference on computer vision and pattern recognition (CVPR)
Wright J, Yang A, Ganesh A, Sastry S, Yu L (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31:210–227
Wu X, Memon N (1996) CALIC - A context based adaptive lossless image codec. ICASSP, IEEE Int Conf Acoust Speech Signal Process - Proc 4:1890–1893
Xing X, Wang K, Lv Z (2015) Fusion of gait and facial features using coupled projections for people identification at a distance. IEEE Signal Process Lett 22(12):2349–2353
Yin L, Wei X, Sun Y, Wang J, Rosato MJ (2006) A 3D facial expression database for facial behavior research. FGR 2006 Proc 7th Int Conf Autom Face Gesture Recognit 2006:211–216
Acknowledgements
This research was supported in part by the National Natural Science Foundation of China(No. 61802105, 61632007, 61976076, 61672404, 61632019, 61751310, 61472301, 61875157 and 61572387), Natural Science Foundation of Anhui Province (No. 1908085QF265), the Fundamental Research Funds of the Central Universities of China (No. SA-ZD160203, JBG160228, JBG160213, K5051399020 and K5051202050), the Industry-University-Academy Cooperation Program of Xidian University-Chongqing IC Innovation Research Institute (No.CQIRI-2021CXY-Y14), and Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2016ZDJC-08).
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Fan, C., Li, F., Jiao, Y. et al. A novel lossless compression framework for facial depth images in expression recognition. Multimed Tools Appl 80, 24173–24183 (2021). https://doi.org/10.1007/s11042-021-10796-1
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DOI: https://doi.org/10.1007/s11042-021-10796-1