Face recognition is a technique that is performed after face detection. The detected human face is compared with the images stored in the database. It extracts features from the input image and matches them with human features stored in the database.
- Import the necessary packages:
import cv2 import numpy as np from sklearn import preprocessing
- Load the encoding and decoding task operators:
class LabelEncoding(object):
# Method to encode labels from words to numbers
def encoding_labels(self, label_wordings):
self.le = preprocessing.LabelEncoder()
self.le.fit(label_wordings) - Implement word-to-number conversion for the input label:
def word_to_number(self, label_wordings):
return int(self.le.transform([label_wordings])[0]) - Convert the input label from a number to word:
def number_to_word(self, label_number):
return self.le.inverse_transform([label_number])[0] - Extract images and labels from the input path:
def...