Robust Real-Time Face Detection

A decision-theoretic generalization of on-line learning and an application to boosting

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Induction of decision trees

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A decision-theoretic generalization of on-line learning and an application to boosting

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A model of saliency-based visual attention for rapid scene analysis

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Neural network-based face detection

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The design and use of steerable filters

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Boosting the margin: a new explanation for the effectiveness of voting methods

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Training support vector machines: an application to face detection

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Irrelevant Features and the Subset Selection Problem

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Example-based learning for view-based human face detection

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Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime

Sendek, A. D., Ransom, B., Cubuk, E. D., Pellouchoud, L. A., Nanda, J., & Reed, E. J. (2022) · Advanced Energy Materials · doi:10.1002/aenm.202200553 · Cited by 100

A comprehensive review of recent advances on deep vision systems

Abbas, Q., Ibrahim, M. E. A., & Jaffar, M. A. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-018-9633-3 · Cited by 99

Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks

Seyedhosseini, M., Sajjadi, M., & Tasdizen, T. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.269 · Cited by 95

Learning-based prediction of gestational age from ultrasound images of the fetal brain

Namburete, A. I. L., Stebbing, R. V., Kemp, B., Yaqub, M., Papageorghiou, A. T., & Alison Noble, J. (2015) · Medical Image Analysis · doi:10.1016/j.media.2014.12.006 · Cited by 97

Visual learning of texture descriptors for facial expression recognition in thermal imagery

Hernández, B., Olague, G., Hammoud, R., Trujillo, L., & Romero, E. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.08.012 · Cited by 97

Portable camera-based assistive text and product label reading from hand-held objects for blind persons

Yi, C., Tian, Y., & Arditi, A. (2014) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2013.2261083 · Cited by 97

Multimodal emotional state recognition using sequence-dependent deep hierarchical features

Barros, P., Jirak, D., Weber, C., & Wermter, S. (2015) · Neural Networks · doi:10.1016/j.neunet.2015.09.009 · Cited by 86

Artificial intelligence in multimodal learning analytics: a systematic literature review

Mohammadi, M., Tajik, E., Martinez-Maldonado, R., Sadiq, S., Tomaszewski, W., & Khosravi, H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100426 · Cited by 85

SWAPPED! Digital face presentation attack detection via weighted local magnitude pattern

Agarwal, A., Singh, R., Vatsa, M., & Noore, A. (2017) · 2017 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2017.8272754 · Cited by 85

Adaptive Fusion of Multi-Scale YOLO for Pedestrian Detection

Hsu, W.-Y., & Lin, W.-Y. (2021) · IEEE Access · doi:10.1109/access.2021.3102600 · Cited by 80

Standard Plane Localization in Ultrasound by Radial Component Model and Selective Search

Ni, D., Yang, X., Chen, X., Chin, C.-T., Chen, S., Heng, P. A., Li, S., Qin, J., & Wang, T. (2014) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2014.06.006 · Cited by 65

Hobbit: Providing Fall Detection and Prevention for the Elderly in the Real World

Bajones, M., Fischinger, D., Weiss, A., Wolf, D., Vincze, M., de la Puente, P., Körtner, T., Weninger, M., Papoutsakis, K., Michel, D., et al. (2018) · Journal of Robotics · doi:10.1155/2018/1754657 · Cited by 64

The cascading neural network: building the Internet of Smart Things

Leroux, S., Bohez, S., De Coninck, E., Verbelen, T., Vankeirsbilck, B., Simoens, P., & Dhoedt, B. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1029-1 · Cited by 59

Transfer learning for pedestrian detection

Cao, X., Wang, Z., Yan, P., & Li, X. (2013) · Neurocomputing · doi:10.1016/j.neucom.2011.12.043 · Cited by 57

On the Intrinsic Dimensionality of Image Representations

Gong, S., Boddeti, V. N., & Jain, A. K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00411 · Cited by 59

Emotion Recognition and Depression Diagnosis by Acoustic and Visual Features: A Multimodal Approach

Sidorov, M., & Minker, W. (2014) · Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge · doi:10.1145/2661806.2661816 · Cited by 56

Comparison between deep learning models and traditional machine learning approaches for facial expression recognition in ageing adults

Caroppo, A., Leone, A., & Siciliano, P. (2020) · Journal of Computer Science and Technology · doi:10.1007/s11390-020-9665-4 · Cited by 60

FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

Chen, L., Zaharia, M., & Zou, J. (2023) · arXiv · doi:10.48550/arxiv.2305.05176 · Cited by 889

Parity models: erasure-coded resilience for prediction serving systems

Kosaian, J., Rashmi, K. V., & Venkataraman, S. (2019) · Proceedings of the 27th ACM Symposium on Operating Systems Principles · doi:10.1145/3341301.3359654 · Cited by 48

DeepSetNet: Predicting Sets with Deep Neural Networks

Rezatofighi, S. H., B. G., V. K., Milan, A., Abbasnejad, E., Dick, A., & Reid, I. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.561 · Cited by 45

Combining body pose, gaze, and gesture to determine intention to interact in vision-based interfaces,

Schwarz, J., Marais, C. C., Leyvand, T., Hudson, S. E., & Mankoff, J. (2014) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2556989 · Cited by 45

Accelerating Mobile Applications at the Network Edge with Software-Programmable FPGAs

Jiang, S., He, D., Yang, C., Xu, C., Luo, G., Chen, Y., Liu, Y., & Jiang, J. (2018) · IEEE INFOCOM 2018 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2018.8485850 · Cited by 40

Design and evaluation of a foveated video streaming service for commodity client devices

Ryoo, J., Yun, K., Samaras, D., Das, S. R., & Zelinsky, G. (2016) · Proceedings of the 7th International Conference on Multimedia Systems · doi:10.1145/2910017.2910592 · Cited by 41

Improved non-maximum suppression for object detection using harmony search algorithm

Song, Y., Pan, Q.-K., Gao, L., & Zhang, B. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.05.005 · Cited by 42

Visual Semantic Information Pursuit: A Survey

Liu, D., Bober, M., & Kittler, J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2950025 · Cited by 39

Visual-Based Human Crowds Behavior Analysis Based on Graph Modeling and Matching

Chen, D.-Y., & Huang, P.-C. (2013) · IEEE Sensors Journal · doi:10.1109/jsen.2013.2245889 · Cited by 31

Selective Search and Sequential Detection for Standard Plane Localization in Ultrasound

Ni, D., Li, T., Yang, X., Qin, J., Li, S., Chin, C.-T., Ouyang, S., Wang, T., & Chen, S. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-41083-3_23 · Cited by 33

Generating Robot/Agent backchannels during a storytelling experiment

Al Moubayed, S., Baklouti, M., Chetouani, M., Dutoit, T., Mahdhaoui, A., Martin, J.-C., Ondas, S., Pelachaud, C., Urbain, J., & Yilmaz, M. (2009) · 2009 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2009.5152572 · Cited by 33

CNN-Based Multimodal Human Recognition in Surveillance Environments

Koo, J. H., Cho, S. W., Baek, N. R., Kim, M. C., & Park, K. R. (2018) · Sensors · doi:10.3390/s18093040 · Cited by 30

Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study

Darabant, A. S., Borza, D., & Danescu, R. (2021) · Mathematics · doi:10.3390/math9020195 · Cited by 24

Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation

Yang, Y., Gupta, A., Feng, J., Singhal, P., Yadav, V., Wu, Y., Natarajan, P., Hedau, V., & Joo, J. (2022) · Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3514094.3534153 · Cited by 24

Transformative Reality: Improving bionic vision with robotic sensing

Lui, W. L. D., Browne, D., Kleeman, L., Drummond, T., & Li, W. H. (2012) · 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc.2012.6345929 · Cited by 23

Spatial Semantic Regularisation for Large Scale Object Detection

Mrowca, D., Rohrbach, M., Hoffman, J., Hu, R., Saenko, K., & Darrell, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.232 · Cited by 20

Deep learning on sparse manifolds for faster object segmentation

Nascimento, J. C., & Carneiro, G. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2725582 · Cited by 17

The image blending method for face swapping

Xingjie, Z., Song, J., & Park, J.-I. (2014) · 2014 4th IEEE International Conference on Network Infrastructure and Digital Content · doi:10.1109/icnidc.2014.7000272 · Cited by 15

Face and marker detection using Gabor frames on GPUs

Găianu, M., & Onchiş, D. M. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.06.029 · Cited by 14

On the improvement of foreground–background model‐based object tracker

Hanif, M. S., Ahmad, S., & Khurshid, K. (2017) · IET Computer Vision · doi:10.1049/iet-cvi.2016.0487 · Cited by 14

Transformative reality: Augmented reality for visual prostheses

Lui, W. L. D., Browne, D., Kleeman, L., Drummond, T., & Li, W. H. (2011) · 2011 10th IEEE International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2011.6092402 · Cited by 12

The Strip Steel Surface Defects Classification Method Based on Weak Classifier Adaptive Enhancement

Yunhui, Y., Kechen, S., Zhitao, X., & Xuehui, F. (2011) · 2011 Third International Conference on Measuring Technology and Mechatronics Automation · doi:10.1109/icmtma.2011.811 · Cited by 11

A Comparative Study Between Deep Learning and Traditional Machine Learning Techniques for Facial Biometric Recognition

Finizola, J. S., Targino, J. M., Teodoro, F. G. S., & de Moraes Lima, C. A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-03928-8_18 · Cited by 9

Benchmarking of Computer Vision Algorithms for Driver Monitoring on Automotive-grade Devices

Battiato, S., Conoci, S., Leotta, R., Ortis, A., Rundo, F., & Trenta, F. (2020) · 2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) · doi:10.23919/aeitautomotive50086.2020.9307437 · Cited by 7

Multi-View Faces Detection Using Viola-Jones Method

Winarno, E., Hadikurniawati, W., Nirwanto, A. A., et al. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1114/1/012068 · Cited by 21

A fast method of face detection in video images

Zhang, L., & Liang, Y. (2010) · International Conference on Advanced Computer Control · doi:10.1109/icacc.2010.5486903 · Cited by 19

Face detection in complex background based on skin color features and improved AdaBoost algorithms

Li, Z., Xue, L., & Tan, F. (2010) · IEEE International Conference on Progress in Informatics and Computing · doi:10.1109/pic.2010.5687939 · Cited by 38

Face Detection Based on Skin Color and Adaboost Arithmetic

Kong, X. (2010) · Modern Electronics Technique

An approach to face detection and recognition

Meena, D., & Saharan, R. (2016) · International Conference on Recent Advances and Innovations in Engineering · doi:10.1109/icraie.2016.7939462 · Cited by 83