Smiling Face Recognition

[abhisheks008]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Smiling Face Recognition
:red_circle: Aim : Create a DL model which will recognize the smiling faces.
:red_circle: Dataset : https://www.kaggle.com/datasets/chazzer/smiling-or-not-face-data
:red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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:red_circle::yellow_circle: Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Email ID :
• Participant ID (if applicable):
• Approach for this Project :
• What is your participant role? (Mention the Open Source program)

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[Rahul-Talari]

Full name : Talari Rahul Mani Datta GitHub Profile Link :https://github.com/Rahul-Talari/ Email ID : 20131a1252@gvpce.ac.in Participant ID (if applicable): Approach for this Project : 1.Algorithm Selection: •Choose VGG16, ResNet50, and MobileNetV2 as the deep learning algorithms for the task of classifying children vs. adults. 2.Dataset Split: • Split the dataset into training and testing sets, with approximately 70% for training and 30% for testing. •Shuffle the dataset before splitting to ensure a representative distribution of samples in both sets. 3.Implementation: • Use TensorFlow or PyTorch as the deep learning framework. • Implement VGG16, ResNet50, and MobileNetV2 architectures. • Configure the models with appropriate settings and adjustments. 4.Training and Validation: • Compile each model with a suitable loss function.

What is your participant role? SSOC Participant

I hope that i can successfully manage this and i also know intel api's to peform better optimization so ,I think , Ican do justification.

[abhisheks008]

As per the Contribution Guidelines and Code of Conduct, one contributor should be assigned to one issue at a time.

[RaginaPhalangae]

Full name : Ayushi Gautam GitHub Profile Link : https://github.com/RaginaPhalangae Email ID : rphalangae@gmail.com Participant ID (if applicable): Approach for this Project :

1. Convolutional Neural Network (CNN) Approach :

• Build a CNN architecture specifically designed for facial expression recognition.
• Preprocess the dataset by normalizing the images and resizing them to a consistent size.
• Split the dataset into training and testing sets.
• Train the CNN using the training set, where the labels indicate whether the face is smiling or not.
• Evaluate the trained model on the testing set to measure its performance.
• Fine-tune the model if necessary by adjusting hyperparameters or modifying the architecture.
• Deploy the trained model to recognize smiling faces in new images.

2. Transfer Learning Approach :

• Utilize a pre-trained CNN model, such as VGG16, ResNet, or Inception, which has been trained on a large-scale image recognition task like ImageNet.
• Remove the last fully connected layers of the pre-trained model and replace them with new layers that are appropriate for smiling face recognition.
• Freeze the weights of the pre-trained layers to prevent them from being updated during training.
• Prepare the dataset and split it into training and testing sets.
• Train the modified model using the training set, where the labels indicate smiling or non-smiling faces.
• Evaluate the performance of the model on the testing set and fine-tune if needed.
• Deploy the model for recognizing smiling faces in new images.

What is your participant role? SSOC Participant

@abhisheks008 Assigning this issue to me will surely give a boost to my open source journey and I'll be able to polish my skills on CNN, OpenCV, etc.

[Abhinavcode13]

Please assign this issue to me @abhisheks008 I have good knowledge in machine learning and python libraries like NumPy pandas etc

[ayushi-ras]

Hey please assign this to me , I will work on OpenCV

[srx089]

Full name: Sarvesh Sant Github profile Link: https://github.com/srx089 Email id: sarveshsant089@gmail.com Participant ID(if applicable): Approach for this project:

1. Preprocessing the Dataset: Examine the characteristics and distribution of the dataset and if needed perform data augmentation or transformation.
2. Algorithm: CNNs are superior for facial expression recognition because they can extract and analyze relevant features and patterns in images, ensuring accurate discrimination can also be checked with ANN. Then train and test the Dataset on these algorithms and determine the performance of the models and compare it
3. Then test the model for smiling or not images. What is your participant role? SSOC Participant

[jatinsahani]

Full name : Jatin Sahani GitHub Profile Link : https://github.com/jatinsahani Email ID : sahanijatin00@gmail.com Participant ID (if applicable): Approach for this Project : Using Facial Landmark Detection Algorithm such as Dlib or OpenCV's Facial Landmark detector.

What is your participant role? (SSOC Participant)

[abhisheks008]

As per the Code of Conduct and Contribution Guidelines issues will be assigned on a first come first serve basis.

Issue assigned to @RaginaPhalangae.

Others can check out the rest of the issues present there in the repository. All the best!

[RaginaPhalangae]

@abhisheks008 Thank youu!!

[aindree-2005]

Full name: Aindree Chatterjee Github profile Link: https://github.com/aindree-2005 Email id: aindree2005@gmail.com Participant ID(if applicable): Approach for this project:

ANN vs CNN vs densenet comparison What is your participant role? Codepeak @abhisheks008 can I work on this?

[abhisheks008]

Issue assigned to you @aindree-2005