Face Recognition MATLAB Projects

IR-Depth Face Detection and Lip Localization Using Kinect V2 Using MATLAB

Face recognition and lip localization are two main building blocks in the development of audio visual automatic speech recognition systems (AV-ASR). In many earlier works, face recognition and lip localization were conducted in uniform lighting conditions with simple backgrounds. However, such conditions are seldom the case in real world applications.

In this paper, we present an approach to face recognition and lip localization that is invariant to lighting conditions. This is done by employing infrared and depth images captured by the Kinect V2 device. First we present the use of infrared images for face detection. Second, we use the face’s inherent depth information to reduce the search area for the lips by developing a nose point detection.


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Robust Unconstrained Face Detection and Lip Localization Algorithm Using Gabor Filters Using MATLAB

Automatic speech recognition (ASR) is a well-researched field of study aimed at augmenting the man-machine interface through interpretation of the spoken word. From in-car voice recognition systems to automated telephone directories, automatic speech recognition technology is becoming increasingly abundant in today’s technological world. Nonetheless, traditional audio-only ASR system performance degrades when employed in noisy environments such as moving vehicles.

To improve system performance under these conditions, visual speech information can be incorporated into the ASR system, yielding what is known as audio-video speech recognition (AVASR). A majority of AVASR research focuses on lip parameters extraction within controlled environments, but these scenarios fail to meet the demanding requirements of most real-world applications. Within the visual unconstrained environment, AVASR systems must compete with constantly changing lighting conditions and background clutter as well as subject movement in three dimensions.


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Face and Lip Localization in Unconstrained Imagery Using MATLAB

When combined with acoustical speech information, visual speech information (lip movement) significantly improves Automatic Speech Recognition (ASR) in acoustically noisy environments. Previous research has demonstrated that visual modality is a viable tool for identifying speech. However, the visual information has yet to become utilized in main stream ASR systems due to the difficulty in accurately tracking lips in real-world conditions. This paper presents our current progress in addressing this issue. We derive several algorithms based on a modified HSI color space to successfully locate the face, eyes, and lips. These algorithms are then tested over imagery collected in visually challenging environments.


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Perspective Distortion Modeling in Face Images and Object Tracking Library Using MATLAB

We describe a method to model perspective distortion as a one- parameter family of warping functions. This can be used to mitigate its effects on visual recognition, or interactively manipulate the perceived personality. The warps are learned from a novel face dataset and, by comparing orbits spanned by images instead of images themselves, we improve face recognition when small focal lengths are used. Additional applications are presented to image editing, videoconference, and multi-view validation of recognition systems. A second chapter is devoted to a new versatile and modular open-source cross- platform online object tracking library, designed to be easily usable by the vision community. Object tracking plays a central part in a number of vision problems, and there is no, to date, a ready-to-use and extensible tracking library at the object level.


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Algorithms and Representations for Visual Recognition Using MATLAB

We address various issues in learning and representation of visual object categories. A key component of many state of the art object detection and image recognition systems, is the image classifier. We first show that a large number of classifiers used in computer vision that are based on comparison of histograms of low level features, are “additive”, and propose algorithms that enable training and evaluation of additive classifiers that offer better trade-offs between accuracy, run-time memory and time complexity than previous algorithms. Our analysis speeds up the training and evaluation of several state of the art object detection, and image classification methods by several orders of magnitude. Many successful object detection algorithms localize an object by simply evaluating a classifier at multiple locations and scales in an image, and finding peaks in the classifier response.


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Parallel Modeling of Fish Interaction Using MATLAB

This paper summarizes our work on a parallel algorithm for an interacting particle model, derived from the model by Czirok, Vicsek, et. al. [4, 5, 6, 15, 16]. Our model is particularly geared toward simulating the behavior of fish in large shoals. In this paper, the background and motivation for the problem are given, as well as an introduction to the mathematical model. A discussion of implementing this model in MATLAB and C++ follows. The parallel implementation is discussed with challenges particular to this mathematical model and how the authors addressed these challenges. Load balancing was performed and is discussed. Finally, a performance analysis follows, using a performance metric to compare the MATLAB , C+ + , and parallelized code.


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The Role of External Features in Face Recognition with Central Vision Loss Using MATLAB

We evaluated how the performance of recognizing familiar face images depends on the internal (eyebrows, eyes, nose, mouth) and external face features (chin, outline of face, hairline) in individuals with central vision loss.


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Attention Modeling for Face Recognition via Deep Learning Using MATLAB

Face recognition is an important area of research in cognitive science and machine learning. This is the first paper utilizing deep learning techniques to model human’s attention for face recognition. In our attention model based on bilinear deep belief network (DBDN), the discriminant information is maximized in a frame of simulating the human visual cortex and human’s perception. Comparative experiments demonstrate that from recognition accuracy our deep learning model outperforms both representative benchmark models and existing bio-inspired models. Furthermore, our model is able to automatically abstract and emphasize the important facial features and patterns which are consistent with the human’s attention map


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Microcontroller based Automative Security System using RFID with Face Recognition Using MATLAB

The project “Microcontroller Based Automative Security System using RFID with Face Recognition” is an advanced autonomous process for security measures that can be applicable to any institution that wishes to monitor and allow access to restricted personnel. The motivation for the project was to eliminate human personnel from the process to make it cost effective especially since it can function non-stop for 24 hours a day. The project incorporates the use of RFID technology to act as a first line of identification for an individual. This is further enhanced by adding a MATLAB algorithm for facial recognition. Since the project was successful in achieving its goal, the results can vouch for this technology to be adapted in all interested institutions in our country.


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A MATLAB based Face Recognition System using Image Processing and Neural Networks Using MATLAB

Automatic recognition of people is a challenging problem which has received much attention during recent years due to its many applications in different fields. Face recognition is one of those challenging problems and up to date, there is no technique that provides a robust solution to all situations. This paper presents a new technique for human face recognition. This technique uses an image-based approach towards artificial intelligence by removing redundant data from face images through image compression using the two-dimensional discrete cosine transform (2D-DCT). The DCT extracts features from face images based on skin color. Feature-vectors are constructed by computing DCT coefficients. A self-organizing map (SOM) using an unsupervised learning technique is used to classify DCT-based feature vectors into groups to identify if the subject in the input image is "present" or "not present" in the image database. Face recognition with SOM is carried out by classifying intensity values of grayscale pixels into different groups. Evaluation was performed in MATLAB using an image database of 25 face images, containing five subjects and each subject having 5 images with different facial expressions. After training for approximately 850 epochs the system achieved a recognition rate of 81.36% for 10 consecutive trials. The main advantage of this technique is its high-speed processing capability and low computational requirements, in terms of both speed and memory utilization.


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