Figure 2 - uploaded by Talari Tirupal
Content may be subject to copyright.
Fingerprint Recognition. Palm Recognition: Palm print acknowledgment is a biometric validation technique dependent on the interesting examples of different qualities in the palms of individuals'
Source publication
Face affirmation from a video is a standard subject in biometrics investigation. Face affirmation development has commonly stood apart due to its huge application worth and market potential, for instance, a nonstop video surveillance structure. It is comprehensively perceived that the face affirmation has expected a huge activity in perception syst...
Context in source publication
Similar publications
CCTV camera system is a very popular method of keeping a building or a place
under surveillance. In Conventional security systems, the cameras only record and
stream the video feed so that someone can watch what happened in that place at
that time. But it can’t prevent a burglary or can’t immediately alert the authorities.
In this project, we propo...
Citations
... The sum of the Fully Connected Layer's output probabilities is 1, the use of Softmax for activation ensures this. Every scalar or vector of real-valued scores may be transformed into a value vector between 0 and 1 by using the Softmax function [8][9][10]. ...
The development of real-time facial recognition software continues to surge forward. Uniquely recognizing human faces in a real-time system relies heavily on face detection and recognition. When it comes to authentication and other forms of security, the face is where it’s at. An improved and faster facial detection system is a primary goal. This work introduces a CNN and Python-based Face Recognition System. The paper presents the analysis of machine learning classification techniques to identify leading predictive algorithms. Further, the algorithms namely Decision Tree, Naïve Bayes, KNN, and CNN analyzed. In the proposed work OpenCV and Python are applied to the dataset after pre-processing images. For this purpose, a celebrity dataset of faces is utilized. In addition, a face or faces caught in the live feed are identified. The process considered two phases for the face recognition system: the training phase and the testing phase. Eighty percent of the human face samples are learned during training, and twenty percent of the data is used for testing. An accuracy of 89.36% is achieved by using machine learning to improve accuracy measures including recall value, f-score, and precision. Comparative performance analysis of these machine learning techniques also performed.
Face identification from picture datasets is the objective of a recent research study. Data from Alan Grant, Claire Dearing, Elliot Sattler, Ian Malcolm, John Hammond and Owen Grady are now being used in the current study. Deep learning were utilized to create facial recognition. According to the results of the simulation, face recognition takes less time when using compressed photos than it did with the prior model. In addition, the suggested task consumes a less amount of storage space. The suggested work’s accuracy is determined to be superior to that of the usual technique. As a result, the suggested study has developed a more efficient method for recognizing several faces.KeywordsFace recognitionPythonImage compressionDeep learningImage processing
