Author Login IJIRT Partner Network (IPN) Login

OPTIMIZING USER RECOGNITION WITH DEEP CNN AND CROPPED FACIAL INPUTS

  • Unique Paper ID: 177473
  • Volume: 11
  • Issue: 12
  • PageNo: 751-755
  • Abstract:
  • Systems for facial recognition have become widely used in fields including attendance tracking, security, and authentication. However, different lighting conditions, background noise, and facial occlusions make it difficult to achieve great accuracy in real-world circumstances. To improve recognition accuracy and model efficiency, this research proposes a deep learning-based method that makes use of a Convolutional Neural Network (CNN) trained just on cropped face areas. Prior to feature extraction, faces are cropped to improve attention on discriminative facial traits and remove irrelevant background data. After separating and aligning face areas using a face detection pipeline, the system sends the information to a deep CNN that has been trained on a labelled dataset for classification. Results from experiments show that, especially in hardware-constrained contexts, the suggested approach greatly enhances recognition performance when compared to models trained on full-frame images. This study offers insights into creating lightweight yet precise recognition algorithms and emphasizes the significance of region-focused preprocessing in deep facial recognition.
Download article email to a friend

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 751-755

OPTIMIZING USER RECOGNITION WITH DEEP CNN AND CROPPED FACIAL INPUTS

Related Articles

Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Latest Publication

Archive

View More

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry