https://www.kaggle.com/niten19/face-shape-dataset
In this report, four distinct challenging scopes are addressed under the supervised machine learning paradigm. They comprise binary classification tasks for gender (A1) and smile detection (A2) along with multi-categorical classification tasks concerning eye-colour (B2) and face-shape (B1) recognition. Most notably, several methodologies are proposed to deal with these duties
| Test 2 | Test 4 | Test 2 | Test 3 | |
|---|---|---|---|---|
| Dataset | Face Shape Set | Face Shape Set | Face Shape Set | Face Shape Set |
| Dataset division | 70:15:15 | 70:15:15 | 60:20:20 | 60:20:20 |
| Original examples | 5.000 images | 5.000 images | 5.000 images | 5.000 images |
| Size of each image | 178x218x3 | 178x218x3 | 500x500x3 | 500x500x3 |
| First operations | None | faces are extracted by means of face_recognition models from images previously converted in grayscale | None | Harmful images are removed with the pre-trained model_glasses specifically designed |
| Examples | Unchanged | 4990 images | Unchanged | 8146 images |
| New image size | Unchanged | 96x48x1 | Unchanged | Unchanged |
| Pre-processing | Images are rescaled and reshaped | HOG features extracted from face images are standardised before being reduced by PCA | Images are rescaled and reshaped | Images are rescaled and reshaped |
| Data augmentation on training dataset | Images are randomly and horizontally flipped | None | None | None |
| Input example shape | 96x96x3 | 360x1 | 224x224x3 | 224x224x3 |
| Model | CNN | CNN | CNN | CNN |
| Batch size | 16 | None | 16 | 16 |
| Epoch | 25 | None | 10 | 10 |
The packages required for the execution of the code along with the role of each file and the software used are described in the Sections below.
The following lists gather all the packages needed to run the project code. Please note that the descriptions provided in this subsection are taken directly from the package source pages. For more details it is reccomended to directly reference to their official sites.
Compulsory :
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Pandas provides fast, flexible, and expressive data structures designed to make working with structured and time series data both easy and intuitive.
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Numpy is the fundamental package for array computing with Python.
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Tensorflow is an open source software library for high performance numerical computation. Its allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs). Important: Recently Keras has been completely wrapped within Tensorflow.
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Pathlib offers a set of classes to handle filesystem paths.
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Shutil provides a number of high-level operations on files and collections of files. In particular, functions are provided which support file copying and removal.
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Os provides a portable way of using operating system dependent functionality.
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Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
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Sklearn offers simple and efficient tools for predictive data analysis.
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Skimage is a collection of algorithms for image processing.
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Random implements pseudo-random number generators for various distributions.
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Cv2 is an open-source library that includes several hundreds of computer vision algorithms.
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Face_recognition is useful to recognize and manipulate faces with the world’s simplest face recognition library. Built from dlib’s state-of-the-art deep learning library.
Optional :
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Comet_ml helps to manage and track machine learning experiments.
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Vprof is a Python package providing rich and interactive visualizations for various Python program characteristics such as running time and memory usage.
PyCharm is an integrated development environment (IDE) for Python programmers: it was chosen because it is one of the most advanced working environments and for its ease of use.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools.
flask is a front end framework that is very well suited for handling pythons end points.
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