implementing simple graphical effects that use the information extracted from the human face achieved through TensorFlow, sklearn, skimage, and Keras. Face segmentation

 

 

 

 

 

 

 

 

 

 

 

FACE  ALIGNMENT AND SEGMENTATION SYSTEM

 

 

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ABSTRACT

Detection, extraction, segmentation, and recognition of face as part of the intelligent living application is very vital in our daily life. Alignment and segmentation are part and play a significant role in face recognition systems. The landmarks derived from the shape of the faces by alignment methods are used in many applications, including real face classification and virtual face animation. In this paper, Sklearn, Keras, Skimage, and TensorFlow methods will be used to train the model with provided datasets, align the image data to identify facial landmarks, and segment data to realize face vector points for the segmented image. 

 

 

 

INTRODUCTION

Image identification is very fundamental in application areas such as Biometric systems and computer vision. The objective of this paper is to implement a Face Segmentation system that can partition a human face into sections, identify and align corresponding landmarks of that image. This paper also focuses on implementing simple graphical effects that use the information extracted from the human face. Face segmentation will be of focus in this paper rather than face detection, which only involves determining whether objects are faced or not.

 

This paper is significant due to many ongoing and emerging applications in the field of computer vision. Such applications include Facial Recognition System, which can identify and recognize human faces uniquely based on pixels properties. Facial recognition system has several applications such as login credentials in android and computer systems, video surveillance using the concept of face tracking, among others.

 

Extensive accelerated research has been done on face segmentation, but still, there is a lot to be fully and convincingly solved. The problem has been fueled by the existence of complex content of images and their applications.

 

This paper is organized into distinct parts, preprocessing, face alignment, face segmentation, and graphical effect implementation. The paper will also have a recommendation and a conclusion.

 

PREPROCESSING

 

Machine learning algorithms will only work with vectors alone. There is, therefore, the need to convert training data into vectors. In this paper, the dataset provided is in matrix form. We will take advantage of the TensorFlow iterator object known as image_data_generator, which creates several variables that can be used to preprocess the data.

 

 

 

 

 

 

 

FACE ALIGNMENT

Face alignment is the process of locating image components or landmarks that describes the face and pose of a face. It is sometimes referred to as feature finding or face landmark detection. Fig 1 and Fig 2 below shows the various landmarks and pose of human and animal faces respectively.                                                       

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