Abstract
Artificial Intelligence and Smart Systems are having an increasing impact on our day to day lives and on how our cities handle our ever-growing needs. Smart systems are systems capable of making decisions based on data received from different sensors and performing actions based on those decisions. These (smart) actions could and should be different for each user depending on their characteristics and needs. Interfaces or interactions with users that employ a “one-size-fits-all” policy should not be considered as contemporary solutions and are not how future interfaces are expected to behave. The user should be “eased in” to the presence of any technology, where the technology adapts to the user, and not the other way around. This paper presents an integrated framework for human attribute classification, which describes people in a non-intrusive way, forgetting the person as soon as he/she ends the interaction so as not to compromise their privacy. With this initial stage of the framework, it is possible to predict a person’s age, gender, height and facially expressed emotions, as well as recognize objects in their possession, e.g., “dog”, wheelchair, cane and suitcase, which can help to characterize their needs. Practical results are presented.
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References
Rodrigues, J.M.F., Martins, M., Sousa, N., Rosa, M.: IoE accessible bus stop: an initial concept. In: Proceedings 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI 2018), pp. 137–143. ACM, New York (2018). https://doi.org/10.1145/3218585.3218659
Rodrigues, J.M.F., Pereira, J.A.R., Sardo, J.D.P., Freitas, M.A.G., Cardoso, P.J.S., Gomes, M., Bica, P.: Adaptive card design UI implementation for an augmented reality museum application. In: Antona, M., Stephanidis, C. (eds.) Universal Access in Human-Computer Interaction 2017, Part I, LNCS 10277, pp. 433–443 (2017). https://doi.org/10.1007/978-3-319-58706-6_35
OpenCV: Open Source Computer Vision Library. https://opencv.org/. Accessed 9 Apr 2019
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR, vol. 1, no. 1, pp. 511–518 (2001)
Face Detection using Haar Cascades. https://docs.opencv.org/4.1.0/d7/d8b/tutorial_py_face_detection.html. Accessed 9 Apr 2019
Face Recognition with OpenCV. https://docs.opencv.org/4.1.0/da/d60/tutorial_face_main.html. Accessed 9 Apr 2019
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 12, 2037–2041 (2006)
Bradski, G.R., Pisarevsky, V.: Intel’s computer vision library: applications in calibration, stereo segmentation, tracking, gesture, face and object recognition. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2000), vol. 2, pp. 796–797 (2000)
Girija, S.S.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. https://www.tensorflow.org/. Accessed 9 Apr 2019
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M.: TensorFlow: a system for large-scale machine learning. In: 12th Symposium on Operating Systems Design and Implementation, pp. 265–283 (2016)
Keras: The Python Deep Learning library. https://keras.io/. Accessed 9 Apr 2019
Zagoruyko, S., Komodakis, N.: Wide Residual Networks. arXiv preprint arXiv:1605.07146 (2016)
Easy Real time gender age prediction from webcam video with Keras. https://www.dlology.com/blog/easy-real-time-gender-age-prediction-from-webcam-video-with-keras/. Accessed 9 Apr 2019
Arriaga, O., Valdenegro-Toro, M., Plöger, P.: Real-time convolutional neural networks for emotion and gender classification. arXiv preprint arXiv:1710.07557 (2017)
Demonstration of Facial Emotion Recognition on Real Time Video Using CNN: Python & Keras. https://appliedmachinelearning.blog/2018/11/28/demonstration-of-facial-emotion-recognition-on-real-time-video-using-cnn-python-keras/. Accessed 9 Apr 2019
Momeni-K, M., Diamantas, S.C., Ruggiero, F., Siciliano, B.: Height estimation from a single camera view. In: Proceedings International Conference on Computer Vision Theory and Applications, pp. 358–364 (2012)
Matsumoto, D., Ekman, P.: Facial expression analysis. Scholarpedia 3(5), 4237 (2008)
Amos, B., Ludwiczuk, B., Satyanarayanan, M.: OpenFace: a general-purpose face recognition library with mobile applications. CMU School of Computer Science (2016)
Baltrusaitis, T., Zadeh, A., Lim, Y.C., Morency, L.P.: OpenFace 2.0: facial behavior analysis toolkit. In: Proceedings 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 59–66. IEEE (2018)
Azure Face API. https://azure.microsoft.com/en-us/services/cognitive-services/face/. Accessed 9 Apr 2019
Real Time Facial Expression Recognition with Deep Learning. https://github.com/a514514772/Real-Time-Facial-Expression-Recognition-with-DeepLearning. Accessed 9 Apr 2019
Gender Classification with OpenCV. https://docs.opencv.org/3.0-last-rst/modules/face/doc/tutorial/facerec_gender_classification.html. Accessed 9 Apr 2019
Age and Gender Classification using Deep Convolutional Neural Networks. https://gilscvblog.com/2015/11/19/age-and-gender-classification-using-deep-convolutional-neural-networks/. Accessed 9 Apr 2019
Annalakshmi, M., Roomi, S.M.M., Naveedh, A.S.: A hybrid technique for gender classification with SLBP and HOG features. Cluster Comput., 1–10 (2018) https://doi.org/10.1007/s10586-017-1585-x
Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34–42 (2015)
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291–7299 (2017)
Bajireanu, R., Pereira, J.A.R., Veiga, R.J.M., Sardo, J.D.P., Cardoso, P.J.S., Lam, R., Rodrigues, J.M.F.: Mobile human shape superimposition: an initial approach using OpenPose. Int. J. Comput. 3, 1–8 (2019)
de Chaumont, F., Marhic, B., Delahoche, L., Cauchois, C.: Generic method for recognition of a wheelchair, even with a low resolution-effective sensor. In: IEEE International Conference on industrial Technology (ICIT) (2004). https://doi.org/10.1109/icit.2004.1490256
Acknowledgments
This work was supported by the Portuguese Foundation for Science and Technology (FCT), project LARSyS (UID/EEA/50009/2019), CIAC (UID/Multi/04019/2019), CIEO and project ACCES4ALL: Accessibility for All in Tourism (SAICT-POL/23700/2016), Portugal2020, CRESC2020, PO Norte 2020, FEDER.
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Turner, D., Rodrigues, J.M.F., Rosa, M. (2020). Describing People: An Integrated Framework for Human Attributes Classification. In: Monteiro, J., et al. INCREaSE 2019. INCREaSE 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-30938-1_26
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DOI: https://doi.org/10.1007/978-3-030-30938-1_26
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