Free Python Library for Facial Analysis & Recognition
Detect 3D Facial Landmarks in Pictures via Open Source Python 3D library. It allows developers to perform Facial Analysis and Recognition inside Python apps.
Facial analysis and recognition have become crucial in numerous applications, including computer vision, biometrics, augmented reality, and emotion detection. A fundamental step in facial analysis is the precise localization of facial landmarks, such as the eyes, nose, and mouth. One remarkable open source library that has gained significant attention in the field of facial landmark localization is Face-Alignment. Developed by a dedicated team of researchers and engineers, this library offers powerful tools and algorithms for accurate and robust face alignment. It is capable of detecting points in both 2D and 3D coordinates.
Face-Alignment is an open-source Python library that provides a comprehensive set of tools and algorithms for facial landmark localization inside Python applications. There are several important features part of the library, such as facial landmark localization, detection and alignment of multiple faces in an image, providing pre-trained models, customization to specific requirements or hardware constraints, easy integration with other libraries, facial expression analysis support, facial tracking for virtual reality (VR), facial animation, character rigging and many more.
The Face-Alignment library provides a comprehensive set of tools and models for facial landmark detection and alignment. It leverages state-of-the-art machine learning techniques, specifically deep learning, to achieve precise facial landmark localization even under challenging conditions, such as occlusions, pose variations, and lighting changes. It employs convolutional Neural Networks (CNNs) to detect and localize facial landmarks. It offers pre-trained models that are capable of identifying a set of key facial landmarks, typically including eyes, eyebrows, nose, mouth, and jawline. The library's ease of use, real-time performance, and customization options make it a preferred choice for a wide range of applications as well as software developers.
Getting Started with Face-Alignment
The easiest way to install Face-Alignment stable release is using pip. Please use the following command for a smooth installation.
Install Face-Alignment via pip
pip install face-alignment You can also install Face-Alignment via Conda using the following command.
conda install -c 1adrianb face_alignmentYou can download the compiled shared library from Github repository.
Detect 2D & 3D Facial Landmarks in Pictures via Python
The Open source Python Library Face-Alignment has included a very powerful feature for detecting 2D & 3D facial landmarks in pictures inside Python applications. Face-Alignment employs Convolutional Neural Networks (CNNs) to detect and localize facial landmarks. It offers pre-trained models that are capable of identifying a set of key facial landmarks, typically including eyes, eyebrows, nose, mouth, and jawline. To achieve the goal first you need to read the image file using OpenCV's imread() function. The following examples shows how to detect 2D and 3D facial landmarks inside pictures using Python code.
Detect 2D Facial Landmarks in Pictures via Python API
import face_alignment
from skimage import io
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, flip_input=False)
input = io.imread('../test/assets/aflw-test.jpg')
preds = fa.get_landmarks(input)
Detect 3D Facial Landmarks in Pictures via Python API
import face_alignment
from skimage import io
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.THREE_D, flip_input=False)
input = io.imread('../test/assets/aflw-test.jpg')
preds = fa.get_landmarks(input)
Multiple Face Detection inside Python Apps
TThe Face-Alignment library has included support for detecting multiple faces using Python API with ease. The library supports the detection and alignment of multiple faces in an image simultaneously without any external dependencies. This feature proves useful in applications where multiple faces need to be processed concurrently thus making it suitable for applications involving group photos or videos with multiple individuals. With its state-of-the-art algorithms, pre-trained models, and modular framework, it simplifies the process of facial landmark localization.