Enhancing Face Detection and Recognition
Nov 7, 2023
Nov 7, 2023
Nov 7, 2023
In the second week of our attendance tracker project, we've made significant strides in our journey towards robust face detection and recognition. Our goal was to 1) finalize the optimal detection model and 2) start exploring face recognition models. We're thrilled to share our progress and insights with you 🚀
Selecting the Right Model 🤖
[Ayesha] - After thorough experimentation on diverse datasets and careful consideration of each model's strengths and weaknesses, we've decided to implement a Dlib HOG model. We examined various recognition models, evaluating factors such as accuracy, efficiency, and suitability for our project.
[Ved] - I explored different face detection models like Faster RCNN, Dlib HOG, Haar Cascade, and MTCNN, customizing and comparing their outputs in various scenarios. Ultimately, Dlib HOG emerged as the best choice due to its high accuracy and reasonable execution time for most photos.
Jump into our Demo 🎥
Technical Insights 🔍
In our exploration of recognition models, we began by researching and compiling a comprehensive list of widely used recognition models. These models were categorized based on their specific use cases, advantages, and disadvantages to gain a holistic understanding of their capabilities and limitations.
After this initial stage, we proceeded to implement and evaluate two prominent face recognition models: VGG Face and FaceNet. VGG Face and FaceNet are well-regarded and extensively utilized in the field of face recognition. The objective was to gain practical experience and insights into their performance, allowing us to make informed decisions regarding their suitability for our project.
This hands-on approach with VGG Face and FaceNet provided valuable exposure to their functionalities, strengths, weaknesses, and overall effectiveness in face recognition tasks. It also allowed us to compare their performance and ascertain how well they align with the specific requirements and objectives of our attendance tracker application.
Challenges and Solutions 💡
[Ayesha] - During the implementation phase, we encountered challenges with the performance of the models, particularly noticing suboptimal results with certain images. In response, we decided to augment the dataset by increasing the number of images used for training the models. This augmentation approach aimed to enhance the model's ability to generalize and accurately recognize faces, especially in cases where the initial results were unsatisfactory.
Notably, FaceNet exhibited promising results with good accuracy even in the face of these challenges. The accuracy achieved with FaceNet demonstrated its robustness and suitability for our project, motivating us to further fine-tune its parameters and optimize its performance.
On the other hand, VGG Face faced challenges in achieving the desired accuracy levels. This indicated the need for additional efforts to fine-tune the VGG Face model. We recognized that further work, adjustments to parameters, or potential architectural modifications might be necessary to improve its performance and align it with our project objectives.
[Ved] - The main challenge I faced during this time period was dealing with out of date/poorly documented modules and previously deprecated stuff.
Key Learnings 🧐
Our experiences underscored the importance of data augmentation and continuous model refinement. While FaceNet exhibited strong accuracy, our work on VGG Face emphasized the iterative nature of model development.
Valuable Resources 📚
We found several resources helpful during our work:
Future Plans 📅
In the upcoming week, we'll expand our exploration of recognition models, aiming to identify even more suitable options. We'll also integrate our detection and recognition components to create a more comprehensive system.
Join Our Tech Journey 🌐
Join us on our tech adventure as we continue to push the boundaries of face detection and recognition. Stay tuned for weekly updates and subscribe to our newsletter to become a part of our growing tech community!
P.S. Found our blog helpful? Share it with friends who might benefit from it and help us grow our community of learners and innovators!
In the second week of our attendance tracker project, we've made significant strides in our journey towards robust face detection and recognition. Our goal was to 1) finalize the optimal detection model and 2) start exploring face recognition models. We're thrilled to share our progress and insights with you 🚀
Selecting the Right Model 🤖
[Ayesha] - After thorough experimentation on diverse datasets and careful consideration of each model's strengths and weaknesses, we've decided to implement a Dlib HOG model. We examined various recognition models, evaluating factors such as accuracy, efficiency, and suitability for our project.
[Ved] - I explored different face detection models like Faster RCNN, Dlib HOG, Haar Cascade, and MTCNN, customizing and comparing their outputs in various scenarios. Ultimately, Dlib HOG emerged as the best choice due to its high accuracy and reasonable execution time for most photos.
Jump into our Demo 🎥
Technical Insights 🔍
In our exploration of recognition models, we began by researching and compiling a comprehensive list of widely used recognition models. These models were categorized based on their specific use cases, advantages, and disadvantages to gain a holistic understanding of their capabilities and limitations.
After this initial stage, we proceeded to implement and evaluate two prominent face recognition models: VGG Face and FaceNet. VGG Face and FaceNet are well-regarded and extensively utilized in the field of face recognition. The objective was to gain practical experience and insights into their performance, allowing us to make informed decisions regarding their suitability for our project.
This hands-on approach with VGG Face and FaceNet provided valuable exposure to their functionalities, strengths, weaknesses, and overall effectiveness in face recognition tasks. It also allowed us to compare their performance and ascertain how well they align with the specific requirements and objectives of our attendance tracker application.
Challenges and Solutions 💡
[Ayesha] - During the implementation phase, we encountered challenges with the performance of the models, particularly noticing suboptimal results with certain images. In response, we decided to augment the dataset by increasing the number of images used for training the models. This augmentation approach aimed to enhance the model's ability to generalize and accurately recognize faces, especially in cases where the initial results were unsatisfactory.
Notably, FaceNet exhibited promising results with good accuracy even in the face of these challenges. The accuracy achieved with FaceNet demonstrated its robustness and suitability for our project, motivating us to further fine-tune its parameters and optimize its performance.
On the other hand, VGG Face faced challenges in achieving the desired accuracy levels. This indicated the need for additional efforts to fine-tune the VGG Face model. We recognized that further work, adjustments to parameters, or potential architectural modifications might be necessary to improve its performance and align it with our project objectives.
[Ved] - The main challenge I faced during this time period was dealing with out of date/poorly documented modules and previously deprecated stuff.
Key Learnings 🧐
Our experiences underscored the importance of data augmentation and continuous model refinement. While FaceNet exhibited strong accuracy, our work on VGG Face emphasized the iterative nature of model development.
Valuable Resources 📚
We found several resources helpful during our work:
Future Plans 📅
In the upcoming week, we'll expand our exploration of recognition models, aiming to identify even more suitable options. We'll also integrate our detection and recognition components to create a more comprehensive system.
Join Our Tech Journey 🌐
Join us on our tech adventure as we continue to push the boundaries of face detection and recognition. Stay tuned for weekly updates and subscribe to our newsletter to become a part of our growing tech community!
P.S. Found our blog helpful? Share it with friends who might benefit from it and help us grow our community of learners and innovators!
Lets build something
great together!
📞 Phone: +91-8329284312
📧 Email: contact@hackerox.com
📍Address:
Malad West, Mumbai - 400064
Lets build something
great together!
📞 Phone: +91-8329284312
📧 Email: contact@hackerox.com
📍Address: Malad West, Mumbai - 400064
Lets build something
great together!
📞 Phone: +91-8329284312
📧 Email: contact@hackerox.com
📍Address: Malad West, Mumbai - 400064