The problem FIKR.NOT solves

The main problem that facial keypoint recognition solves is enabling computers to understand and analyze human faces more accurately and efficiently.Facial keypoint recognition has a wide range of applications, including facial expression recognition, face tracking and alignment, face detection, and 3D face modeling. It is used in various fields, such as security, entertainment, healthcare, and marketing.

Face recognition technology can be used in security and surveillance systems around the world to enhance public safety and prevent crime.
The scope of identity verification through face recognition is being used in various industries for security and authentication purposes.
Face recognition technology has the potential to transform marketing and advertising by enabling more personalized and targeted approaches.
Face recognition technology is becoming increasingly prevalent in the travel industry, particularly in border security and airport operations.

For example, in security, facial keypoint recognition can be used to identify individuals by matching their facial features to a database of known faces. In entertainment, it can be used to animate virtual characters with realistic facial expressions. In healthcare, it can be used to detect and track facial features to diagnose certain conditions such as autism or Parkinson's disease. In marketing, it can be used to analyze customer reactions to products or advertisements based on their facial expressions.

Overall, facial keypoint recognition is an important problem to solve as it helps to improve our understanding and interaction with human faces through technology.

Challenges we ran into

As freshers in the field of Machine Learning, our team was faced with a new enterprise that required us to learn how to train and test data models, optimize code, integrate various code domains, and use Canva for slide deck creation. These challenges were not easy to overcome, but we worked consistently and put in a lot of hard work and effort to learn and improve our skills.

One of the main challenges we faced was learning how to train and test data models. We had to understand the basics of machine learning algorithms, data preprocessing, and feature engineering. We also had to learn how to use popular tools like Scikit-learn, TensorFlow, and Keras to build and evaluate our models.

Another challenge we faced was optimizing our code to run faster and more efficiently. We had to learn how to use different optimization techniques, such as vectorization, parallelization, and GPU acceleration. We also had to learn how to use tools like profiling and debugging to identify and fix performance issues.

Integrating various code domains was another challenge we faced. We had to learn how to use different programming languages and libraries, such as Python, R, and MATLAB, to work with different data formats and tools. We also had to learn how to integrate our code with web services and databases to access and process large amounts of data.

Finally, we had to learn how to use Canva to create visually appealing and effective slide decks for our presentations. We had to learn how to use different design elements, such as colors, fonts, and images, to communicate our ideas and data effectively to our audience.

Overall, the challenges we faced as freshers in the field of Machine Learning were significant, but we overcame them through consistent hard work and a non-stop learning process. We gained valuable experience and skills that will help us in our future endeavors in this exciting and rapidly evolving field.