Abstract
In this paper, we have proposed an efficient and computationally inexpensive approach toward two mainstreams of image recognition, i.e, face recognition and person identification. Our proposed model is invariant to pose, expression, scale, illumination, and translation with the application of different techniques and implementation of their algorithms. For the purpose of making the model illumination invariant, we have preprocessed the image with linear transformation. Features are extracted from preprocessed facial images using a Modified-Local Difference Binary descriptor which incorporates gradient knowledge from a nonlinear scale space. The extracted facial features are then quantized to a vector. Subsequently, an extreme gradient boosting algorithm is used, resulting in fast and high-performance classification. The proposed method has been experimented on three benchmark datasets like GRIMACE, FACES95, FACES96 producing significant results in terms of speed, accuracy, and efficiency. We extended our proposed method and tested it on Raspberry Pi 3 to conclude that it is fast on limited processor and memory settings. This pipeline has resulted in a faster and efficient face recognition approach with decrease in error rate around +8 to +10%.
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References
D.G. Lowe, Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
E. Rublee, V. Rabaud, K. Konolige, G. Bradski, ORB: an efficient alternative to SIFT or SURF, in 2011 IEEE International Conference on Computer Vision (ICCV) (IEEE, 2011), pp. 2564–2571
E. Mair, G.D. Hager, D. Burschka, M. Suppa, G. Hirzinger. Adaptive and generic corner detection based on the accelerated segment test, in European Conference on Computer Vision (Springer, Berlin, 2010), pp. 183–196
M. Calonder, V. Lepetit, C. Strecha, P. Fua, Brief: binary robust independent elementary features, in Computer Vision ECCV 2010 (2010), pp. 778–792
M.A. Hearst, S.T. Dumais, E. Osuna, J. Platt, B. Scholkopf, Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998)
E.I. Altman, G. Marco, F. Varetto, Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the Italian experience). J. Bank. Finan. 18(3), 505–529 (1994)
M. Okawa, Offline signature verification based on bag-of-visual words model using KAZE features and weighting schemes, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2016), pp. 184–190
W. Li, Y. Qian, M. Loomes, X. Gao, The application of KAZE features to the classification echocardiogram videos, in Multimodal Retrieval in the Medical Domain (Springer International Publishing, 2015), pp. 61–72
K.L. Flores-Rodrguez, F. Trujillo-Romero. Free form object recognition module using A-KAZE and GCS
L. Caramazana, R. Arroyo, L.M. Bergasa, Visual odometry correction based on loop closure detection, in Open Conference on Future Trends in Robotics (RoboCity16) (2016), pp. 97–104
O. Slizovskaia, E. Gómez Gutiérrez, G. Haro Ortega, Automatic musical instrument recognition in audiovisual recordings by combining image and audio classification strategies, in Proceedings SMC 2016. 13th Sound and Music Computing Conference; 2016 Aug 31; Hamburg, Germany, ed. by R. Großmann, G. Hajdu. Hamburg (Germany): ZM4, Hochschule fr Musik und Theater Hamburg; 2016. pp. 442–4477. Zentrum fr Mikrotonale Musik und Multimediale Komposition (ZM4), Hochschule fr Musik und Theater Hamburg, 2016
S. Madisetty, M.S. Desarkar, An ensemble based method for predicting emotion intensity of tweets, in International Conference on Mining Intelligence and Knowledge Exploration (Springer, Cham, 2017), pp. 359-370
X. Ren, H. Guo, S. Li, S. Wang, J. Li, A novel image classification method with CNN-XGBoost model, in International Workshop on Digital Watermarking (Springer, Cham, 2017), pp. 378–390
M. Penev, O. Boumbarov, Facial landmark detection using ensemble of cascaded regressions. Int. J. 128 (2015)
X.-P. Huynh, S.-M. Park, Y.-G. Kim, Detection of driver drowsiness using 3D deep neural network and semi-supervised gradient boosting machine, in Asian Conference on Computer Vision (Springer, Cham, 2016), pp. 134–145
K. Xu, D. Feng, H. Mi, Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image. Molecules 22(12), 2054 (2017)
P.F. Alcantarilla, T. Solutions, Fast explicit diffusion for accelerated features in nonlinear scale spaces. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1281–1298 (2011)
S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: spatial pyramid matching for recognizing natural scene categories, in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2 (IEEE, 2006), pp. 2169–2178
J. Yang, K. Yu, Y. Gong, T. Huang, Linear spatial pyramid matching using sparse coding for image classification, in IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009 (IEEE, 2009), pp. 1794–1801
J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, Y. Gong, Locality-constrained linear coding for image classification, in 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2010), pp. 3360–3367
T. Chen, C. Guestrin, Xgboost: a scalable treeboosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2016), pp. 785–794
G. Hermosilla, J. Ruiz-del-Solar, R. Verschae, M. Correa, A comparative study of thermal face recognition methods in unconstrained environments. Pattern Recogn. 45(7), 2445–2459 (2012)
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Vinay, A. et al. (2019). Face Recognition using Invariant Feature Vectors and Ensemble of Classifiers. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds) Soft Computing and Signal Processing . Advances in Intelligent Systems and Computing, vol 900. Springer, Singapore. https://doi.org/10.1007/978-981-13-3600-3_74
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DOI: https://doi.org/10.1007/978-981-13-3600-3_74
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