What are the key steps to building a facial recognition portfolio?

Building a facial recognition portfolio involves several key steps. First, understand the technology by learning about face detection, feature extraction, and matching techniques. Next, collect and preprocess a diverse dataset of facial images. Extract relevant features from these images and train a facial recognition model. Implement face detection and create a database of known faces. Finally, integrate your model into an application, test it thoroughly, and consider privacy and ethical considerations. Good luck with your project! ????

Building a facial recognition portfolio requires showcasing your skills and experience in developing or utilizing facial recognition technology. Below are the steps to start working. Development: If you're a developer, highlight your experience building facial recognition algorithms, integrating them into applications, or contributing to open-source projects. Deployment: If you focus on deploying facial recognition, showcase your expertise in choosing appropriate technologies, implementing them securely, and addressing ethical considerations. Analysis: If your strength lies in analyzing facial recognition data, emphasize your skills in extracting insights, evaluating accuracy, and interpreting results.

Tal vez este el punto fundamental y más importante es escoger correctamente el problema que pueda resolverse con la tecnología actual. Por ejemplo, el uso de reconocimiento facial está usandose cada vez más para validaciones biométricas en bancos, gobiernos, etc. Esto ha funcionado bien porque normalmente se usa en un entorno controlado con cámaras de celular de buena resolución y con buena iluminación y sin elementos que generen oclusión en el rostro. Sin embargo, el uso de esta misma tecnología para detectar delincuentes en las calles no ha funcionado tan bien y mas bien han generado quejas por los errores que generan dado que sucede en un ambiente no controlado.

While building your own model is very important for strengthen your portfolio, learning, hands-on experimenting, and comparing the performance of existing state-of-the-art facial recognition services, such as AWS Rekognition or Azure AI can also significantly benefit your expertise.

In this step below will be the key steps. Code Samples (Developers): Showcase well-documented code snippets or links to public repositories demonstrating your facial recognition development skills. Case Studies (Deployment/Analysis): Describe real-world deployments (if possible) or create case studies analyzing facial recognition applications, highlighting its strengths and limitations. Data Visualizations: Utilize charts, graphs, or other visuals to effectively communicate the results of your facial recognition analysis.

As you train the model, it is imperative you follow regulatory compliance as it relates to facial recognition as much of the application is banned- specifically in the EU with the AI Act. Following the framework that creates an equitable model, and follows regulation is key.

Once your model performs well on the testing set, deploy it in a real-world environment. Test the model's accuracy and efficiency on new, unseen data. Monitor and address any issues that arise during deployment, such as bias or privacy concerns.

Create a portfolio showcasing the results, insights, and lessons learned from your facial recognition project. Include visualizations, performance metrics, and any improvements made over time. Regularly update your portfolio with new projects, research findings, or advancements in facial recognition technology.

As discussed in Unmasking AI by Dr. Joy Buolamwini, be mindful of looking at error rate in aggregate. She found that in facial detection systems as much as 30 percentage point differences in the error rates for light/pale men compared to dark women. This can lead to significant issues such as failure of self driving cars to detect dark women.

Additional Points which need to be take care during this whole journey. Stay Updated: Demonstrate your awareness of current advancements and ethical considerations surrounding facial recognition technology. Explain the "Why": Briefly explain the purpose and motivation behind your projects, showcasing your problem-solving approach. Maintain Confidentiality: If working with proprietary data, ensure your portfolio content adheres to any confidentiality agreements.