Face detection Project Help

Face detection is one of the most fundamental aspects of computer vision. It is the base of many further studies like identifying specific people to marking key points on the face.  At CodersArts we provide assignment help on all sorts of facial detection systems using technologies like 

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Haar Cascades: The haarcascade_frontalface_default. XML is a haar cascade designed by OpenCV to detect the frontal face. A Haar Cascade works by training the cascade on thousands of negative images with the positive image superimposed on it. The haar cascade is capable of detecting features from the source.

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Dlib Frontal Face Detector:​ It's a landmark's facial detector with pre-trained models, the dlib is used to estimate the location of 68 coordinates (x, y) that map the facial points on a person's face.

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DNN Face Detector in OpenCV: With the release of OpenCV 3.3 the deep neural network ( dnn. ) library has been substantially overhauled, allowing us to load pre-trained networks via the Caffe, TensorFlow, and Torch/PyTorch frameworks and then use them to classify input images and detect the contours in each frame where the face is located.

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MTCNN (Multi-task Cascaded Convolutional Neural Networks): MTCNN is mostly used alongside the pre-trained facet model to embed the faces of all the participants in the image. This Joint Face Detection Alignment is able to simultaneously propose bounding boxes, five-point facial landmarks, and detection probabilities.

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Dlib HOG based frontal face detector: Another attractive feature of Dlib is its HOG based models. These models are meant to be a good “frontal” face detector and it is, indeed. It detects faces even when they are not perfectly frontal to a good extent.

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Tasks Associated with Face Detection:

• Pose: The images of a face vary due to the relative camera-face pose (frontal, 45 degree, profile, upside down), and some facial features such as an eye or the nose may become partially or wholly occluded.

• Presence or absence of structural components: Facial features such as beards, mustaches, and glasses may or may not be present and there is a great deal of variability among these components including shape, color, and size. 

• Facial expression: The appearance of faces is directly affected by a person’s facial expression. 

• Occlusion: Faces may be partially occluded by other objects. In an image with a group of people, some faces may partially occlude other faces. 

• Image orientation: Face images directly vary for different rotations about the camera’s optical axis. 

• Imaging conditions: When the image is formed, factors such as lighting (spectra, source distribution and intensity) and camera characteristics (sensor response, lenses) affect the appearance of a face.

• Face Matching: Find the best match for a given face.

• Face Similarity: Find faces that are most similar to a given face.

• Face Transformation: Generate new faces that are similar to a given face.

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Transfer Learning For Image Classification Project Help

(TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, the knowledge gained while learning to recognize cars could apply when trying to recognize trucks. Through our traditional methods, the need for huge amounts of data is essential but this is not always the case sometimes we have only less amount of data at our hand. To deal with this lack of data, we make use of a technique called transfer learning. Transfer learning is a method that allows us to use the knowledge gained from other tasks in order to tackle new but similar problems quickly and effectively. This reduces the need for data related to the specific task we are dealing with. There are many pre-trained models available on the internet which could be used to extract features from the dataset in case the amount of data available is low.

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Types of Transfer learning

• Freeze Convolutional Base Model

• Train selected top layers in the base model

• Combination of steps a and b.

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Optimization Techniques for Transfer Learning

• Learning Rate

• Model Architecture

• Type of transfer learning

• Optimisation technique

• Regularisation

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Choice of Pre-Trained Models

• vgg16-19

• ResNet

• InceptionV3,

• ResNet,

• MobileNet,

• Xception,

• InceptionResNetV2

• Facenet

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Generalized Steps Involved to Perform Transfer Learning Task on Vision Data

• Define a model

• Find ideal initial learning rate

• Create a module for scheduling the learning rate

• Augment the Images

• Apply the transformation(mean subtraction) for better fine-tuning

• Test on a smaller set

• Fit the model

• Test the model on random images

• Visualize the kernels to validate if the training has been successful.

Optical Character Recognition(OCR) Project Help

One well-known application of A.I. is Optical Character Recognition (OCR).OCR, is a technology that enables you to convert different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera into editable and searchable data.  Tesseract does various image processing operations internally (using the Leptonica library) before doing the actual OCR. OCR has been around for decades, and its most common use is to convert an image into searchable text. Obviously, the accuracy of the conversion is important, at Codersarts most OCRs provide 98 to 99 percent accuracy, measured at the page level.

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A list of state of art OCR’s are presented in the list below:

1. OmniPage Ultimate

2. Abbyy FineReader

3. Adobe Acrobat Pro DC

4. Readiris

5. Rossum

At Codersarts we also build custom OCR using frameworks like cv2 & pytesseract. There are numerous other public domain OCR libraries. However, the best omni-font OCR libraries are not public domain libraries. All modern OCR packages make use of Omni-font based recognition capability. In python for example, use of the skimage functions like Regionprops, label, Clear_border, threshold_otsu, hog (Histogram of Gradients) to feed a Chars74k classifier.

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Gesture recognition Project Help

Gesture recognition is a type of perceptual computing user interface that allows computers to capture and interpret human gestures as commands. The general definition of gesture recognition is the ability of a computer to understand gestures and execute commands based on those gestures. Gesture recognition based on the computer vision obtains the picture containing the human hand by cameras and then utilizes the image processing and machine learning to contribute to the judgment and recognition of the gesture comprehensively.

At present, the steps of gesture recognition based on computer graphics are split into the following stages:

• image collection

• hand detection

• segmentation

• gesture recognition

• classification

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Types of algorithms involved in building gesture-based models.

• Skeletal-based algorithms

• Appearance-based models

• Electromyography-based models

• 3D model-based algorithms

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The interaction between humans and robots constantly evolves and adopts different tools and software to increase the comfort of humans. The OpenCV library is not enough to start your project. This library provides you the software side, but you also need hardware components. As for  hardware category enters a developed platform able to run the OpenCV library, webcams, and 3D sensors such as Kinect 3D. The OpenCV is a free and open-source library focused on real-time image processing.It can detect and recognize a large variety of objects, but in this scenario, our focus mostly is to apply techniques and methods to detect and recognize the gestures of a human hand.

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Human Pose Estimation Project Help

Human Pose estimation is an important problem that has enjoyed the attention of the Computer Vision community for the past few decades. It is a crucial step towards understanding people in images and videos. Human Pose Estimation is defined as the problem of localization of human joints (also known as keypoints - elbows, wrists, etc) in images or videos. It is also defined as the search for a specific pose in space of all articulated poses.

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Types of Pose Estimation Systems:

• 2D Pose Estimation - Estimate a 2D pose (x,y) coordinates for each joint from an RGB image.

• 3D Pose Estimation - Estimate a 3D pose (x,y,z) coordinates a RGB image.

Human Pose Estimation has some pretty cool applications and is heavily used in Action recognition, Animation, Gaming, etc. For example, a very popular Deep Learning app HumanCourt uses Pose Estimation to analyze Basketball player movements. Some of the most successful methodologies involved in Computer Vision-based Pose Estimation are listed below:

• DeepPose

• Efficient Object Localization Using Convolutional Networks

• Convolutional Pose Machines

• Human Pose Estimation with Iterative Error Feedback

• Deep High-Resolution Representation Learning for Human Pose Estimation

• Stacked Hourglass Network For Human Pose Estimation

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Smart Traffic Light System Using Deep learning

Traffic congestion is a huge problem in almost every developing country as the people using private vehicles are increasing each day and the capacity of the road networks is still not up to the mark. Vehicular traffic problems are very common in urban areas as both private vehicles and other public transportation services are huge in number due to the dense population. This problem affects the functioning of the city. Every individual has to schedule his/her day within the 24 hours time limit.One of the core components of a smart city is automated traffic management. And that got me thinking – could I use my data science chops to build a vehicle detection model that could play a part in smart traffic management?

Think about it – if you could integrate a vehicle detection system in a traffic light camera, you could easily track the number of useful things simultaneously:

• How many vehicles are present at the traffic junction during the day?

• What time does the traffic build-up?

• What kind of vehicles is traversing the junction (heavy vehicles, cars, etc.)?

• Is there a way to optimize the traffic and distribute it through a different street?

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However, traffic volumes in urban areas kill the potential time of the individuals. Also, huge amounts of fuel are wasted due to the increasing waiting time, particularly at signal points. Additionally, many urban areas are facing severe air pollution issues. This has a very high impact on the health and well-being of society. To address this issue, we need a better and efficient infrastructure of the city and the proper management of road traffic. Nowadays, artificial intelligence (AI) and Computer vision are playing an important role in solving many of the real-world problems. We may use these Machine Learning techniques to address road traffic management problems. As the manual maintenance is difficult and not sufficient with the increasing number of vehicles on roads, automation of traffic signal management with ML may result in better traffic conditions in urban areas.

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The main idea behind this is to divide the system into the following phases.

In the first phase, we classify the traffic signal junctions into one of the three different zones.

• High-level

• Medium-level

• Low-level traffic zones

In phase second we use classification algorithms like Support Vector Machine, to classify the traffic into given zones. In the third phase, we optimize the signal configuration of high-level traffic zones to bring them to either medium-level or low-level traffic zones. Models like this are mostly built using TensorFlow, sklearn along with a few other utility packages like skimage.

Machine Learning is solutions for all whether you are student, small or Medium level enterprises. You can see Machine Learning everywhere, Machine Learning development happen in the finance and insurance domain, industries, and healthcare.Here are some of the common usage of Machine Learning in the real world:

• Image recognition

• Speech recognition

• Recommendation systems

• Medical Diagnosis

• Statistical Calculations

• Classification

• Prediction

• Extraction

• Regression

• Robotics - ROS

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