Get your deep learning proposal work from high end trained professionals. The passion of your areas of interest will be clearly reflected in your proposal. Chose an expert to provide you with custom research proposal work. To interpret the real-time process of the art, historical context and future scopes we have made a literature survey in Deep Learning (DL).

1. Define Objectives:

• Clearly sketch what we need to execute with our comprehensive view.
• Take transformers in Natural Language Processing (NLP) as an example and note its specific tasks and issues.

2. Primary Sources:

• Research Databases: We can use the fields such as Google Scholar, arXIv, PubMed (for biomedical papers), IEEE Xplore, and others.
• Conference: Here NeurIPS, ICML, ICLR, CVPR, ICCV, ACL, EMNLP are the basic conferences in DL.
• Journal: The Journal of Machine Learning Research (JMLR) and Neural Computation are the papers frequently establish DL related studies.

3. Start by Reviews and Surveys:

• Find the latest survey and review papers on our area of interest which gives a literature outline and frequently see the seminal latest works.
• Begin with Convolutional Neural Networks (CNNs) architecture survey paper if we search for CNN.

4. Reading Papers:

• Skim: Begin with reading abstracts, introductions, conclusions, and figures.
• Deep Dive: When a study shows high similar to our work, then look in-depth to its methodology, experiments, and results.
• Take Notes: Look down the basic plans, methods, datasets, Evaluation metrics, and open issues described in the paper and note it.

5. Forward and Backward Search:

• Forward: We can detect how the area is emerging using the tools such as Google Scholar’s “Cited by” feature to find latest papers in our research.
• Backward: We can track the improvement of designs by seeing the reference which is gives more knowledge in our study.  

6. Organize and Combine:

• Classify the papers by its themes, methodologies and version.
• We have to analyze the trends, patterns, and gaps in the literature.

7. Keep Updates:

• We need to stay update with notifications on fields such as Google Scholar and arXiv for keywords similar to our title with the recent publications, because Dl is a fast-emerging area.

8. Tools and Platforms:

• Utilize the tools such as Mendeley, Zotero and EndNote for maintaining and citing papers.
• We find similar papers with AI-driven suggestions from Semantic Scholar platform.

9. Engage with the Community:

• Join into mailing lists, social media groups and online conference to get related with DL. Websites such as Reddit’s r/Machine Learning or the AI Alignment Forum frequently gather latest papers.
• By attending the webinars, workshops and meetings often can help us to gain skills from recent techniques and find knowledge of what the group seems essential.

10. Report and Share:

• If we want to establish the paper make annotated bibliographies, presentations, and review papers based on our motive and file the research.
• We can our scope to help others and publish us a skilled person in this topic.

            The objective of this review is to crucially recognize and integrate the real-time content in the area. Though it is a time-consuming work, it will be useful for someone aims to make research and latest works in DL.

Deep Learning project face recognition with python OpenCV

            Designing a face remembering system using Python and OpenCV is an amazing work that introduces us into the world of computer vision and DL. The following are the step-by-step guide to construct a simple face recognition system:

1. Install Necessary Libraries

Make sure that we have the required libraries installed:

bash

pip install opencv-python opencv-python-headless

2. Capture Faces

We require a dataset for training. We utilize the pre-defined dataset and capture our own using OpenCV.

python

import cv2

cam = cv2.VideoCapture(0)

detector = cv2.CascadeClassifier(cv2.data.haarcascades + ‘haarcascade_frontalface_default.xml’)

id = input(‘Enter user ID: ‘)

sampleNum = 0

while True:

    ret, img = cam.read()

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    faces = detector.detectMultiScale(gray, 1.3, 5)

    for (x,y,w,h) in faces:

        sampleNum += 1

        cv2.imwrite(f”faces/User.{id}.{sampleNum}.jpg”, gray[y:y+h,x:x+w])

        cv2.rectangle(img, (x,y), (x+w, y+h), (255,0,0), 2)

        cv2.waitKey(100)

    cv2.imshow(‘Capture’, img)

    cv2.waitKey(1)

    if sampleNum > 20: # capture 20 images

        break

cam.release()

cv2.destroyAllWindows()

3. Training the Recognizer

OpenCV has a built-in face recognizer. For this example, we’ll use the LBPH (Local Binary Pattern Histogram) face recognizer.

python

import cv2

import numpy as np

from PIL import Image

import os

path = ‘faces’

recognizer = cv2.face.LBPHFaceRecognizer_create()

detector = cv2.CascadeClassifier(cv2.data.haarcascades + ‘haarcascade_frontalface_default.xml’)

def getImagesAndLabels(path):

    imagePaths = [os.path.join(path,f) for f in os.listdir(path)]    

    faceSamples=[]

    ids = []

    for imagePath in imagePaths:

        PIL_img = Image.open(imagePath).convert(‘L’)

        img_numpy = np.array(PIL_img,’uint8′)

        id = int(os.path.split(imagePath)[-1].split(“.”)[1])

        faces = detector.detectMultiScale(img_numpy)

        for (x,y,w,h) in faces:

           faceSamples.append(img_numpy[y:y+h,x:x+w])

            ids.append(id)

    return faceSamples, np.array(ids)

faces,ids = getImagesAndLabels(path)

recognizer.train(faces, ids)

recognizer.save(‘trainer/trainer.yml’)

4. Recognizing Faces

python

import cv2

import numpy as np

recognizer = cv2.face.LBPHFaceRecognizer_create()

recognizer.read(‘trainer/trainer.yml’)

cascadePath = cv2.data.haarcascades + “haarcascade_frontalface_default.xml”

faceCascade = cv2.CascadeClassifier(cascadePath)

font = cv2.FONT_HERSHEY_SIMPLEX

cam = cv2.VideoCapture(0)

minW = 0.1*cam.get(3)

minH = 0.1*cam.get(4)

while True:

    ret, img = cam.read()

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    faces = faceCascade.detectMultiScale(

        gray,

        scaleFactor=1.2,

        minNeighbors=5,

        minSize=(int(minW), int(minH)),

    )

    for (x,y,w,h) in faces:

        id, confidence = recognizer.predict(gray[y:y+h,x:x+w])

        if (confidence < 100):

            confidence = f”  {round(100 – confidence)}%”

        else:

            id = “unknown”

            confidence = f”  {round(100 – confidence)}%”

        cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)

        cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1) 

    cv2.imshow(‘Face Recognition’,img)

    if cv2.waitKey(1) & 0xFF == ord(‘q’):

        break

cam.release()

cv2.destroyAllWindows()

We have proper directories (faces and trainer) to design. It will be a basic face recognition system and can strengthen with DL models for better accuracy and robustness against various states in real-time. To achieve better accuracy in real-time conditions, we discover latest DL based techniques like FaceNet or pre-trained models from DL frameworks.

Deep learning MS Thesis topics

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