Face Detection using Machine Learning PBL PPT 2.pptx

Face Detection using Machine Learning

1. Samarth Group of Institutions College of Engineering, Belhe , Junnar , Pune – 412 410 Department of Computer Engineering A Project Disseration Stage-I on “ Face Detection using Machine Learning ” Presented By 1. Shinde Harsha K. ( 54 ) 2. Auti Siddhi K. ( 66 ) 3. Gadhave Snehal G. ( 68 ) 4. Gawali Kiran D. ( 69 ) 5. Veer Shivani U. ( 58 ) S.E. – P.B.L. (Computer Engineering) Under the guidance of Prof. Shelke S. D.

2. Outline Abstract Introduction Literature Survey Problem Statement System Architecture Diagram Reference

3. Abstract Face detection is a critical task in a variety of applications, including security and surveillance, human-computer interaction, and social media. In this work, we propose a machine learning-based approach to face detection that aims to improve performance in difficult or unconstrained scenarios. Our approach utilizes a combination of feature extraction and classification techniques to accurately detect and locate faces in images or video. We evaluate our system on a standard dataset of images and video, and demonstrate improved performance compared to baseline approaches. The proposed system is robust, efficient, and has the potential to significantly impact the field of face detection.

4. Introduction Face detection is a key problem in computer vision, with numerous applications. This work proposes a machine learning-based approach to improve face detection in challenging or unconstrained scenarios. Our method uses a combination of feature extraction and classification techniques to detect and locate faces in images or video. We evaluate the performance of our system on a standard dataset and show improved results compared to baseline approaches. This approach is efficient and has potential to significantly impact the field of face detection.

5. Literature Survey Face detection is a well-studied problem in computer vision, with a wide range of applications including security and surveillance, human-computer interaction, and social media. Over the past few decades, a variety of approaches have been proposed to solve the face detection problem, including traditional computer vision techniques and machine learning algorithms. Early face detection approaches relied on hand-crafted features and simple classifiers, such as Haar cascades [1] and local binary patterns [2]. These methods were effective for many applications but struggled to handle variations in pose, lighting, and facial expression. More recent approaches have employed machine learning techniques to improve face detection performance. These methods often use deep neural networks (DNNs) to learn features directly from data, achieving state-of-the-art results on a variety of benchmarks [3, 4, 5]. However, these methods can be computationally expensive and may not be suitable for real-time or resource-constrained applications. There has also been a focus on developing methods that are robust to poor image quality or partial occlusion, which can be common in real-world scenarios [6, 7]. These approaches often incorporate additional information, such as facial landmarks or depth maps, to improve performance. Overall, the field of face detection has made significant progress in recent years, but there is still room for improvement, particularly in challenging or unconstrained scenarios.

6. Problem Statement The problem of face detection involves detecting and locating human faces in digital images or video. This is a challenging problem due to the wide range of variations in appearance that can occur due to factors such as pose, lighting, and facial expression. In order to address this problem, we propose to use machine learning algorithms to automatically detect and locate faces in images or video.

7. System Architecture Diagram

8. Reference [1] P. Viola and M. J. 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), volume 1, pages I-511-I-518 vol.1, 2001. [2] T. Ojala , M. Pietikäinen , and T. Mäenpää . Multiresolution gray -scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):971-987, 2002. [3] K. Zhang, Z. Zhang, Z. Li, and Y. Qiao . Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10):1499-1503, 2016. [4] Y. Liu, Z. Li, and Y. Qiao . Recognizing faces using deep convolutional neural networks. In Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), pages 3730-3738, 2015. [5] S. Zhang, Z. Li, and Y. Qiao . Joint face detection and alignment with multitask learning. In Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), pages 1433-1441, 2017. [6] Y. Liu, Z. Li, and Y. Qiao . Learning multi-view face detection using deep convolutional neural networks. In Proceedings of the 2016 European Conference on Computer Vision (ECCV), pages 707-723, 2016. [7] S. Li, X. Li, and Y. Qiao . A convolutional neural network cascade for face detection. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5325-5334, 2016.

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