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.
What is Face-Alignment Library ?
Analyzing and recognizing faces is really important in various fields like computer vision, biometrics, augmented reality, and emotion detection. One key part of this process is accurately pinpointing specific facial features like the eyes, nose, and mouth. A standout open-source tool making waves in this area is Face-Alignment. Created by a group of passionate researchers and engineers, this tool provides a solid foundation for pinpointing facial landmarks effectively. The library offers a wide range of tools and models to detect and align facial landmarks. It uses advanced machine learning methods, especially deep learning, to accurately pinpoint facial landmarks even in difficult situations like when parts of the face are covered, different poses, or changes in lighting. By utilizing Convolutional Neural Networks (CNNs), it can identify and position facial landmarks effectively. Imagine having access to pre-trained models that can recognize essential facial features like eyes, eyebrows, nose, mouth, and jawline.
Face-Alignment is a Python library that offers a wide range of tools and algorithms for finding facial landmarks in Python programs. This library includes key features like detecting and aligning facial landmarks, finding multiple faces in an image, using pre-trained models, adapting to specific needs or hardware limits, integrating smoothly with other libraries, and supporting facial expression analysis and tracking for virtual reality (VR), facial animation, character rigging and many more. It is capable of detecting points in both 2D and 3D coordinates using its advanced algorithm. This library is user-friendly, provides real-time results, and allows for customization, making it a popular option for various applications and 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.
How to 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.