A Systematic Mapping on Detection of Human Mouth Landmarks
Resumo
Facial landmarks represent regions of interest whose detection and localization generate features supporting the identification of movements, feelings, and reactions. Most facial feature detection algorithms focus on entire semantic areas, such as the region of a mouth which allows grained manipulation that is essential for a wide domain variety. This paper describes a systematic mapping of the detection of landmarks in human faces and their application domains. The identification and selection methods of primary studies include automatic search on information sources, inclusion, and exclusion criteria over 344 scientific papers from 2015 and 2021, from which we analyzed and synthesized 115 primary studies. Our analysis considered the implementation of methods, types, and uses of data extracted from the mouth. The mapping brought exciting information as new methods, datasets, and domains researched through the time interval reviewed as well research gaps that can be explored.
Palavras-chave:
systematic mapping, human face, detection and recognition of landmark
Referências
K. Lekdioui, R. Messoussi, Y. Ruichek, Y. Chaabi, and R. Touahni, “Facial decomposition for expression recognition using texture/shape descriptors and svm classifier,” Signal Processing: Image Communication, vol. 58, pp. 300-312, 2017.
S. Anwar, M. Milanova, A. Bigazzi, L. Bocchi, and A. Guazzini, “Real time intention recognition,” in IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, 10 2016, pp. 1021-1024.
A. Juhong and C. Pintavirooj, “Face recognition based on facial landmark detection,” vol. 2017-January, Hokkaido, Japan, 2017, pp. 1-4.
L. Wang, H. Zhang, and Z. Wang, in Component based representation for face recognition, vol. 3, Qingdao, China, 2015, pp. 1275-1278.
B. Reddy, Y. Kim, S. Yun, C. Seo, and J. Jang, “Real-time driver drowsiness detection for embedded system using model compression of deep neural networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 7 2017, pp. 438-445.
L. Zhao, Z. Wang, X. Wang, and Q. Liu, “Driver drowsiness detection using facial dynamic fusion information and a dbn,” IET Intelligent Transport Systems, vol. 12, no. 2, pp. 127-133, 2018.
R. Zheng, C. Tian, H. Li, M. Li, and W. Wei, “Fatigue detection based on fast facial feature analysis,” vol. 9315, Gwangju, Korea, Republic of, 2015, pp. 477-487.
D. Cui, G.-B. Huang, and T. Liu, “Elm based smile detection using distance vector,” Pattern Recognition, vol. 79, pp. 356-369, 2018.
P. Wu, H. Liu, C. Xu, Y. Gao, Z. Li, and X. Zhang, “How do you smile? towards a comprehensive smile analysis system,” Neurocomputing, vol. 235, pp. 245-254, 2017.
X. Min, G. Zhai, and K. Gu, “Visual attention on human face,” in 2015 Visual Communications and Image Processing (VCIP), 12 2015, pp. 1-4.
Y. Ren, Z. Wang, M. Xu, H. Dong, and S. Li, in Learning Dynamic GMM for Attention Distribution on Single-Face Videos, vol. 2017-July, Honolulu, HI, United states, 2017, pp. 1632-1641.
O.-M. Foong, K.-W. Hong, and S.-P. Yong, “Droopy mouth detection model in stroke warning,” Kuala Lumpur, Malaysia, 2016, pp. 616-621.
Y. Zhuang, O. Uribe, M. Mcdonald, I. Lin, D. Arteaga, W. Dalrymple, B. Worrall, A. Southerland, and G. Rohde, “Pathological facial weakness detection using computational image analysis,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 4 2018, pp. 261-264.
R. Angulu, A. O. Adewumi, and J.-R. Tapamo, “Landmark localization approach for facial computing,” Umhlanga, Durban, South africa, 2017.
K. R. Felizardo, E. Y. Nakagawa, S. C. P. F. Fabri, and F. C. Ferrari, Revisão Sistemática da Literatura em Engenharia de Software, 1st ed. Elsevier, 2017.
K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, “Systematic mapping studies in software engineering.” in EASE, vol. 8, 2008, pp. 68-77.
B. Johnston and P. Chazal, “A review of image-based automatic facial landmark identification techniques,” EURASIP Journal on Image and Video Processing, vol. 2018, p. 86, 09 2018.
M. Bodini, “A review of facial landmark extraction in 2d images and videos using deep learning,” Big Data and Cognitive Computing, vol. 3, no. 1, 2019.
Y. Wu and Q. Ji, “Facial landmark detection: A literature survey,” Int. J. Comput. Vision, vol. 127, no. 2, p. 115-142, Feb. 2019.
S. C. P. F. Fabbri, K. R. Felizardo, F. C. Ferrari, E. C. M. Hernandes, F. R. Octaviano, E. Y. Nakagawa, and J. C. Maldonado, “Externalising tacit knowledge of the systematic review process,” IET Software, vol. 7, no. 6, pp. 298-307, 2013.
K. BA and S. Charters, “Guidelines for performing systematic literature reviews in software engineering,” vol. 2, 01 2007.
W. T. Freeman and M. Roth, “Orientation histograms for hand gesture recognition,” MERL - Mitsubishi Electric Research Laboratories, Cambridge, MA 02139, Tech. Rep. TR94-03, 12 1994.
T. Soukupová and J. Cech, “Real-time eye blink detection using facial landmarks,” 2016.
C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, no. 3, pp. 273-297, 9 1995.
P. Awasekar, M. Ravi, S. Doke, and Z. Shaikh, “Driver fatigue detection and alert system using non-intrusive eye and yawn detection,” International Journal of Computer Applications, vol. 180, pp. 1-5, 05 2018.
F. Becerra-Riera, H. Méndez-Vázquez, A. Morales-Gonzalez, and M. Tistarelli, “Age and gender classification using local appearance descriptors from facial components,” in 2017 IEEE International Joint Conference on Biometrics (IJCB), 10 2017, pp. 799-804.
C.-Y. Chang, M.-J. Cheng, and M. H.-M. Ma, “Application of machine learning for facial stroke detection,” vol. 2018-November, Shanghai, China, 2018.
S. G. Shivashankar and S. Hiremath, “Emotion sensing using facial recognition,” Bengaluru, India, 2017, pp. 830-833.
Z. H. Aung and P. Ritthipravat, in Robust visual voice activity detection using Long Short-Term Memory recurrent neural network, vol. 9431, Auckland, New zealand, 2016, pp. 380-391.
S. Afshar and A. A. Salah, “Facial expression recognition in the wild using improved dense trajectories and fisher vector encoding,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 06 2016, pp. 1517-1525.
Z. Xie, Y. Jin, P. Bian, and W. Zhou, “Facial landmark detection via self-adaption model and multi-task feature learning,” vol. 0, Chengdu, China, 2016, pp. 113-117.
W. Zhang, W. Wang, S. Zhao, and B. Sun, “Gray-edge-hog feature based cascaded learning for facial landmark detection,” vol. 189, Beijing, China, 2018.
A. Kumar and R. Patra, in Driver drowsiness monitoring system using visual behaviour and machine learning, Penang Island, Malaysia, 2018, pp. 339-344.
P. Bian, Z. Xie, and Y. Jin, “Multi-task feature learning-based improved supervised descent method for facial landmark detection,” Signal, Image and Video Processing, vol. 12, no. 1, pp. 17-24, 2018.
I. Gupta, N. Garg, A. Aggarwal, N. Nepalia, and B. Verma, “Real-time driver's drowsiness monitoring based on dynamically varying threshold,” Noida, India, 2018.
Y. Zhang, F. Jiang, and R. Shen, in Region-Based Face Alignment with Convolution Neural Network Cascade, vol. 10636 LNCS, Guangzhou, China, 2017, pp. 300-309.
X. Chang and W. Skarbek, “Facial expressions recognition by animated motion of candide 3d model,” vol. 10808, Wilga, Poland, 2018, pp. ARIES - Accelerator Research and Innovation for European Science and Society (CERN, EU H2020); Committee of Electronics and Telecommunications, Polish Academy of Sciences; EuroFusion Collaboration; EuroFusion Poland; PKOpto - Polish Committee of Optoelectronics of SEP-The Association of Polish Electrical Engineers; PSP - Photonics Society of Poland.
B. S. Riggan, N. J. Short, and S. Hu, in Thermal to Visible Synthesis of Face Images Using Multiple Regions, vol. 2018-January, Lake Tahoe, NV, United states, 2018, pp. 30-38.
K. Lekdioui, Y. Ruichek, R. Messoussi, Y. Chaabi, and R. Touahni, “Facial expression recognition using face-regions,” Fez, Morocco, 2017, p. CNRST; et al.; Faculty of Medicine and Pharmacy of Fez; Faculty of Sciences of Fez; TICSM; University of Sidi Mohamed Ben Abdellah of Fez.
C.-Y. Chang, M.-J. Cheng, and M.-M. Ma, “Application of machine learning for facial stroke detection,” vol. 2018-November, 2019.
V. Guttha, H. Kondakindi, and V. Bhatti, “Automated feedback generation system using facial emotion recognition,” 2018, pp. 975-981.
S. Mohanty, S. Hegde, S. Prasad, and J. Manikandan, “Design of realtime drowsiness detection system using dlib,” 2019.
A. Kumar and R. Patra, “Driver drowsiness monitoring system using visual behaviour and machine learning,” 2018, pp. 339-344.
V. Venkata Sai Vardhan, N. Ritish Kumar Reddy, K. Jaya Surya, J. Uday Kiran, and A. Kumar, “Driver's drowsiness detection based on facial multi-feature fusion,” vol. 1998, no. 1, 2021.
S. Shivashankar and S. Hiremath, “Emotion sensing using facial recognition,” 2018, pp. 830-833.
J. Josephine Julina and T. Sharmila, “Facial emotion recognition in videos using hog and lbp,” 2019, pp. 56-60.
Z. Xie, Y. Jin, P. Bian, and W. Zhou, “Facial landmark detection via self-adaption model and multi-task feature learning,” vol. 0, 2016, pp. 113-117.
M. Santosh and A. Sharma, “Fusion of multi representation and multi descriptors for facial expression recognition,” vol. 1057, no. 1, 2021.
W. Zhang, W. Wang, S. Zhao, and B. Sun, “Gray-edge-hog feature based cascaded learning for facial landmark detection,” vol. 189, 2018.
Y. Fei, B. Li, H. Wang, and L. Tian, “Long short-term memory network based fatigue detection with sequential mouth feature,” 2020, pp. 217-222.
P. Bian, Z. Xie, and Y. Jin, “Multi-task feature learning-based improved supervised descent method for facial landmark detection,” Signal, Image and Video Processing, vol. 12, no. 1, pp. 17-24, 2018.
S. Dey, S. Chowdhury, S. Sultana, M. Hossain, M. Dey, and S. Das, “Real time driver fatigue detection based on facial behaviour along with machine learning approaches,” 2019, pp. 135-140.
S. Dey, M. Islam, S. Chowdhury, M. Islam, M. Ali Hossain, and S. Das, “Real time tracking of driver fatigue and inebriation maintaining a strict driving schedule,” 2019, pp. 9-14.
A. M. Al-madani, A. T. Gaikwad, V. Mahale, Z. A. Ahmed, and A. A. A. Shareef, “Real-time driver drowsiness detection based on eye movement and yawning using facial landmark,” in 2021 International Conference on Computer Communication and Informatics (ICCCI), Jan 2021, pp. 1-4.
I. Gupta, N. Garg, A. Aggarwal, N. Nepalia, and B. Verma, “Real-time driver's drowsiness monitoring based on dynamically varying threshold,” 2018.
T. Zhang, Z. Chen, and C. Ouyang, “Research on driver fatigue detection,” vol. 10609, 2017.
Z. Aung and P. Ritthipravat, “Robust visual voice activity detection using long short-term memory recurrent neural network,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9431, pp. 380-391, 2016.
S. Mehta, P. Mishra, A. Bhatt, and P. Agarwal, “Ad3s: Advanced driver drowsiness detection system using machine learning,” vol. 2019- November, 2019, pp. 108-113.
A.-C. Phan, N.-H.-Q. Nguyen, T.-N. Trieu, and T.-C. Phan, “An efficient approach for detecting driver drowsiness based on deep learning,” Applied Sciences (Switzerland), vol. 11, no. 18, 2021.
P. Inthanon and S. Mungsing, “Detection of drowsiness from facial images in real-time video media using nvidia jetson nano,” 2020, pp. 246-249.
S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” in 2017 International Conference on Engineering and Technology (ICET), 2017, pp. 1-6.
P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, 12 2001, pp. I-I.
E. Fix and J. L. Hodges, “Discriminatory analysis. nonparametric discrimination: Consistency properties,” International Statistical Review / Revue Internationale de Statistique, vol. 57, no. 3, pp. 238-247, 1989.
C. Sagonas, E. Antonakos, G. Tzimiropoulos, S. Zafeiriou, and M. Pantic, “300 faces in-the-wild challenge: database and results,” Image and Vision Computing, vol. 47, 01 2016.
V. Le, J. Brandt, Z. Lin, L. Bourdev, and T. S. Huang, “Interactive facial feature localization,” in Proceedings of the 12th European Conference on Computer Vision - Volume Part III, ser. ECCV'12. Berlin, Heidelberg: Springer-Verlag, 2012, pp. 679-692.
P. N. Belhumeur, D. W. Jacobs, D. J. Kriegman, and N. Kumar, “Localizing parts of faces using a consensus of exemplars,” in CVPR 2011, 2011, pp. 545-552.
X. Zhu and D. Ramanan, “Face detection, pose estimation, and landmark localization in the wild,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 2879-2886.
A. Atla, R. Tada, V. Sheng, and N. Singireddy, “Sensitivity of different machine learning algorithms to noise,” J. Comput. Sci. Coll., vol. 26, no. 5, p. 96-103, May 2011.
V. Vapnik, The Support Vector Method of Function Estimation. Boston, MA: Springer US, 1998, pp. 55-85.
S. Bozinovski, “Reminder of the first paper on transfer learning in neural networks, 1976,” Informatica, vol. 44, no. 3, Sep. 2020.
L. Torrey and J. Shavlik, “Transfer learning,” in Handbook of research on machine learning applications and trends: algorithms, methods, and techniques. IGI global, 2010, pp. 242-264.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1345-1359, 2009.
S. Anwar, M. Milanova, A. Bigazzi, L. Bocchi, and A. Guazzini, “Real time intention recognition,” in IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, 10 2016, pp. 1021-1024.
A. Juhong and C. Pintavirooj, “Face recognition based on facial landmark detection,” vol. 2017-January, Hokkaido, Japan, 2017, pp. 1-4.
L. Wang, H. Zhang, and Z. Wang, in Component based representation for face recognition, vol. 3, Qingdao, China, 2015, pp. 1275-1278.
B. Reddy, Y. Kim, S. Yun, C. Seo, and J. Jang, “Real-time driver drowsiness detection for embedded system using model compression of deep neural networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 7 2017, pp. 438-445.
L. Zhao, Z. Wang, X. Wang, and Q. Liu, “Driver drowsiness detection using facial dynamic fusion information and a dbn,” IET Intelligent Transport Systems, vol. 12, no. 2, pp. 127-133, 2018.
R. Zheng, C. Tian, H. Li, M. Li, and W. Wei, “Fatigue detection based on fast facial feature analysis,” vol. 9315, Gwangju, Korea, Republic of, 2015, pp. 477-487.
D. Cui, G.-B. Huang, and T. Liu, “Elm based smile detection using distance vector,” Pattern Recognition, vol. 79, pp. 356-369, 2018.
P. Wu, H. Liu, C. Xu, Y. Gao, Z. Li, and X. Zhang, “How do you smile? towards a comprehensive smile analysis system,” Neurocomputing, vol. 235, pp. 245-254, 2017.
X. Min, G. Zhai, and K. Gu, “Visual attention on human face,” in 2015 Visual Communications and Image Processing (VCIP), 12 2015, pp. 1-4.
Y. Ren, Z. Wang, M. Xu, H. Dong, and S. Li, in Learning Dynamic GMM for Attention Distribution on Single-Face Videos, vol. 2017-July, Honolulu, HI, United states, 2017, pp. 1632-1641.
O.-M. Foong, K.-W. Hong, and S.-P. Yong, “Droopy mouth detection model in stroke warning,” Kuala Lumpur, Malaysia, 2016, pp. 616-621.
Y. Zhuang, O. Uribe, M. Mcdonald, I. Lin, D. Arteaga, W. Dalrymple, B. Worrall, A. Southerland, and G. Rohde, “Pathological facial weakness detection using computational image analysis,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 4 2018, pp. 261-264.
R. Angulu, A. O. Adewumi, and J.-R. Tapamo, “Landmark localization approach for facial computing,” Umhlanga, Durban, South africa, 2017.
K. R. Felizardo, E. Y. Nakagawa, S. C. P. F. Fabri, and F. C. Ferrari, Revisão Sistemática da Literatura em Engenharia de Software, 1st ed. Elsevier, 2017.
K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, “Systematic mapping studies in software engineering.” in EASE, vol. 8, 2008, pp. 68-77.
B. Johnston and P. Chazal, “A review of image-based automatic facial landmark identification techniques,” EURASIP Journal on Image and Video Processing, vol. 2018, p. 86, 09 2018.
M. Bodini, “A review of facial landmark extraction in 2d images and videos using deep learning,” Big Data and Cognitive Computing, vol. 3, no. 1, 2019.
Y. Wu and Q. Ji, “Facial landmark detection: A literature survey,” Int. J. Comput. Vision, vol. 127, no. 2, p. 115-142, Feb. 2019.
S. C. P. F. Fabbri, K. R. Felizardo, F. C. Ferrari, E. C. M. Hernandes, F. R. Octaviano, E. Y. Nakagawa, and J. C. Maldonado, “Externalising tacit knowledge of the systematic review process,” IET Software, vol. 7, no. 6, pp. 298-307, 2013.
K. BA and S. Charters, “Guidelines for performing systematic literature reviews in software engineering,” vol. 2, 01 2007.
W. T. Freeman and M. Roth, “Orientation histograms for hand gesture recognition,” MERL - Mitsubishi Electric Research Laboratories, Cambridge, MA 02139, Tech. Rep. TR94-03, 12 1994.
T. Soukupová and J. Cech, “Real-time eye blink detection using facial landmarks,” 2016.
C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, no. 3, pp. 273-297, 9 1995.
P. Awasekar, M. Ravi, S. Doke, and Z. Shaikh, “Driver fatigue detection and alert system using non-intrusive eye and yawn detection,” International Journal of Computer Applications, vol. 180, pp. 1-5, 05 2018.
F. Becerra-Riera, H. Méndez-Vázquez, A. Morales-Gonzalez, and M. Tistarelli, “Age and gender classification using local appearance descriptors from facial components,” in 2017 IEEE International Joint Conference on Biometrics (IJCB), 10 2017, pp. 799-804.
C.-Y. Chang, M.-J. Cheng, and M. H.-M. Ma, “Application of machine learning for facial stroke detection,” vol. 2018-November, Shanghai, China, 2018.
S. G. Shivashankar and S. Hiremath, “Emotion sensing using facial recognition,” Bengaluru, India, 2017, pp. 830-833.
Z. H. Aung and P. Ritthipravat, in Robust visual voice activity detection using Long Short-Term Memory recurrent neural network, vol. 9431, Auckland, New zealand, 2016, pp. 380-391.
S. Afshar and A. A. Salah, “Facial expression recognition in the wild using improved dense trajectories and fisher vector encoding,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 06 2016, pp. 1517-1525.
Z. Xie, Y. Jin, P. Bian, and W. Zhou, “Facial landmark detection via self-adaption model and multi-task feature learning,” vol. 0, Chengdu, China, 2016, pp. 113-117.
W. Zhang, W. Wang, S. Zhao, and B. Sun, “Gray-edge-hog feature based cascaded learning for facial landmark detection,” vol. 189, Beijing, China, 2018.
A. Kumar and R. Patra, in Driver drowsiness monitoring system using visual behaviour and machine learning, Penang Island, Malaysia, 2018, pp. 339-344.
P. Bian, Z. Xie, and Y. Jin, “Multi-task feature learning-based improved supervised descent method for facial landmark detection,” Signal, Image and Video Processing, vol. 12, no. 1, pp. 17-24, 2018.
I. Gupta, N. Garg, A. Aggarwal, N. Nepalia, and B. Verma, “Real-time driver's drowsiness monitoring based on dynamically varying threshold,” Noida, India, 2018.
Y. Zhang, F. Jiang, and R. Shen, in Region-Based Face Alignment with Convolution Neural Network Cascade, vol. 10636 LNCS, Guangzhou, China, 2017, pp. 300-309.
X. Chang and W. Skarbek, “Facial expressions recognition by animated motion of candide 3d model,” vol. 10808, Wilga, Poland, 2018, pp. ARIES - Accelerator Research and Innovation for European Science and Society (CERN, EU H2020); Committee of Electronics and Telecommunications, Polish Academy of Sciences; EuroFusion Collaboration; EuroFusion Poland; PKOpto - Polish Committee of Optoelectronics of SEP-The Association of Polish Electrical Engineers; PSP - Photonics Society of Poland.
B. S. Riggan, N. J. Short, and S. Hu, in Thermal to Visible Synthesis of Face Images Using Multiple Regions, vol. 2018-January, Lake Tahoe, NV, United states, 2018, pp. 30-38.
K. Lekdioui, Y. Ruichek, R. Messoussi, Y. Chaabi, and R. Touahni, “Facial expression recognition using face-regions,” Fez, Morocco, 2017, p. CNRST; et al.; Faculty of Medicine and Pharmacy of Fez; Faculty of Sciences of Fez; TICSM; University of Sidi Mohamed Ben Abdellah of Fez.
C.-Y. Chang, M.-J. Cheng, and M.-M. Ma, “Application of machine learning for facial stroke detection,” vol. 2018-November, 2019.
V. Guttha, H. Kondakindi, and V. Bhatti, “Automated feedback generation system using facial emotion recognition,” 2018, pp. 975-981.
S. Mohanty, S. Hegde, S. Prasad, and J. Manikandan, “Design of realtime drowsiness detection system using dlib,” 2019.
A. Kumar and R. Patra, “Driver drowsiness monitoring system using visual behaviour and machine learning,” 2018, pp. 339-344.
V. Venkata Sai Vardhan, N. Ritish Kumar Reddy, K. Jaya Surya, J. Uday Kiran, and A. Kumar, “Driver's drowsiness detection based on facial multi-feature fusion,” vol. 1998, no. 1, 2021.
S. Shivashankar and S. Hiremath, “Emotion sensing using facial recognition,” 2018, pp. 830-833.
J. Josephine Julina and T. Sharmila, “Facial emotion recognition in videos using hog and lbp,” 2019, pp. 56-60.
Z. Xie, Y. Jin, P. Bian, and W. Zhou, “Facial landmark detection via self-adaption model and multi-task feature learning,” vol. 0, 2016, pp. 113-117.
M. Santosh and A. Sharma, “Fusion of multi representation and multi descriptors for facial expression recognition,” vol. 1057, no. 1, 2021.
W. Zhang, W. Wang, S. Zhao, and B. Sun, “Gray-edge-hog feature based cascaded learning for facial landmark detection,” vol. 189, 2018.
Y. Fei, B. Li, H. Wang, and L. Tian, “Long short-term memory network based fatigue detection with sequential mouth feature,” 2020, pp. 217-222.
P. Bian, Z. Xie, and Y. Jin, “Multi-task feature learning-based improved supervised descent method for facial landmark detection,” Signal, Image and Video Processing, vol. 12, no. 1, pp. 17-24, 2018.
S. Dey, S. Chowdhury, S. Sultana, M. Hossain, M. Dey, and S. Das, “Real time driver fatigue detection based on facial behaviour along with machine learning approaches,” 2019, pp. 135-140.
S. Dey, M. Islam, S. Chowdhury, M. Islam, M. Ali Hossain, and S. Das, “Real time tracking of driver fatigue and inebriation maintaining a strict driving schedule,” 2019, pp. 9-14.
A. M. Al-madani, A. T. Gaikwad, V. Mahale, Z. A. Ahmed, and A. A. A. Shareef, “Real-time driver drowsiness detection based on eye movement and yawning using facial landmark,” in 2021 International Conference on Computer Communication and Informatics (ICCCI), Jan 2021, pp. 1-4.
I. Gupta, N. Garg, A. Aggarwal, N. Nepalia, and B. Verma, “Real-time driver's drowsiness monitoring based on dynamically varying threshold,” 2018.
T. Zhang, Z. Chen, and C. Ouyang, “Research on driver fatigue detection,” vol. 10609, 2017.
Z. Aung and P. Ritthipravat, “Robust visual voice activity detection using long short-term memory recurrent neural network,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9431, pp. 380-391, 2016.
S. Mehta, P. Mishra, A. Bhatt, and P. Agarwal, “Ad3s: Advanced driver drowsiness detection system using machine learning,” vol. 2019- November, 2019, pp. 108-113.
A.-C. Phan, N.-H.-Q. Nguyen, T.-N. Trieu, and T.-C. Phan, “An efficient approach for detecting driver drowsiness based on deep learning,” Applied Sciences (Switzerland), vol. 11, no. 18, 2021.
P. Inthanon and S. Mungsing, “Detection of drowsiness from facial images in real-time video media using nvidia jetson nano,” 2020, pp. 246-249.
S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” in 2017 International Conference on Engineering and Technology (ICET), 2017, pp. 1-6.
P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, 12 2001, pp. I-I.
E. Fix and J. L. Hodges, “Discriminatory analysis. nonparametric discrimination: Consistency properties,” International Statistical Review / Revue Internationale de Statistique, vol. 57, no. 3, pp. 238-247, 1989.
C. Sagonas, E. Antonakos, G. Tzimiropoulos, S. Zafeiriou, and M. Pantic, “300 faces in-the-wild challenge: database and results,” Image and Vision Computing, vol. 47, 01 2016.
V. Le, J. Brandt, Z. Lin, L. Bourdev, and T. S. Huang, “Interactive facial feature localization,” in Proceedings of the 12th European Conference on Computer Vision - Volume Part III, ser. ECCV'12. Berlin, Heidelberg: Springer-Verlag, 2012, pp. 679-692.
P. N. Belhumeur, D. W. Jacobs, D. J. Kriegman, and N. Kumar, “Localizing parts of faces using a consensus of exemplars,” in CVPR 2011, 2011, pp. 545-552.
X. Zhu and D. Ramanan, “Face detection, pose estimation, and landmark localization in the wild,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 2879-2886.
A. Atla, R. Tada, V. Sheng, and N. Singireddy, “Sensitivity of different machine learning algorithms to noise,” J. Comput. Sci. Coll., vol. 26, no. 5, p. 96-103, May 2011.
V. Vapnik, The Support Vector Method of Function Estimation. Boston, MA: Springer US, 1998, pp. 55-85.
S. Bozinovski, “Reminder of the first paper on transfer learning in neural networks, 1976,” Informatica, vol. 44, no. 3, Sep. 2020.
L. Torrey and J. Shavlik, “Transfer learning,” in Handbook of research on machine learning applications and trends: algorithms, methods, and techniques. IGI global, 2010, pp. 242-264.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1345-1359, 2009.
Publicado
22/11/2021
Como Citar
RISSATO, Pedro Henrique D’Almeida G.; BULCÃO-NETO, Renato de Freitas; MACEDO, Alessandra Alaniz.
A Systematic Mapping on Detection of Human Mouth Landmarks. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 17. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 82-87.
DOI: https://doi.org/10.5753/wvc.2021.18894.
