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face-detection-with-age

This project uses TensorFlow in Python for face detection and age prediction. A pre-trained CNN model, such as MTCNN, is used for detecting faces in images. Once a face is detected, another CNN model predicts the age of the individual. This application can be used for security systems, demographic analysis, and personalized user experiences.

Import required modules

import cv2 as cv import math import time from google.colab.patches import cv2_imshow

def getFaceBox(net, frame, conf_threshold=0.7): frameOpencvDnn = frame.copy() frameHeight = frameOpencvDnn.shape[0] frameWidth = frameOpencvDnn.shape[1] blob = cv.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False)

net.setInput(blob)
detections = net.forward()
bboxes = []
for i in range(detections.shape[2]):
    confidence = detections[0, 0, i, 2]
    if confidence > conf_threshold:
        x1 = int(detections[0, 0, i, 3] * frameWidth)
        y1 = int(detections[0, 0, i, 4] * frameHeight)
        x2 = int(detections[0, 0, i, 5] * frameWidth)
        y2 = int(detections[0, 0, i, 6] * frameHeight)
        bboxes.append([x1, y1, x2, y2])
        cv.rectangle(frameOpencvDnn, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight/150)), 8)
return frameOpencvDnn, bboxes

faceProto = "modelNweight/opencv_face_detector.pbtxt" faceModel = "modelNweight/opencv_face_detector_uint8.pb"

ageProto = "modelNweight/age_deploy.prototxt" ageModel = "modelNweight/age_net.caffemodel"

genderProto = "modelNweight/gender_deploy.prototxt" genderModel = "modelNweight/gender_net.caffemodel"

MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746) ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)'] genderList = ['Male', 'Female']

Load network

ageNet = cv.dnn.readNet(ageModel, ageProto) genderNet = cv.dnn.readNet(genderModel, genderProto) faceNet = cv.dnn.readNet(faceModel, faceProto)

padding = 20

def age_gender_detector(frame): # Read frame t = time.time() frameFace, bboxes = getFaceBox(faceNet, frame) for bbox in bboxes: # print(bbox) face = frame[max(0,bbox[1]-padding):min(bbox[3]+padding,frame.shape[0]-1),max(0,bbox[0]-padding):min(bbox[2]+padding, frame.shape[1]-1)]

    blob = cv.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
    genderNet.setInput(blob)
    genderPreds = genderNet.forward()
    gender = genderList[genderPreds[0].argmax()]
    ageNet.setInput(blob)
    agePreds = ageNet.forward()
    age = ageList[agePreds[0].argmax()]

    label = "{},{}".format(gender, age)
    cv.putText(frameFace, label, (bbox[0], bbox[1]-10), cv.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv.LINE_AA)
return frameFace

input = cv.imread("image.jpg") output = age_gender_detector(input) cv2_imshow(output)