Age and gender prediction are used extensively in the field of computer vision for surveillance. Advances in computer vision have made this prediction much more practical and accessible to the general public. Because of its utility in intelligent real-world applications, this study topic has seen significant advancements.
A person’s identity, age, gender, emotions, and ethnicity are all determined by the traits on their face. Security and video surveillance, electronic customer relationship management, biometrics, electronic vending machines, human-computer interface, entertainment, cosmetology, and forensic art are just a few of the real-world applications where age and gender classification might come in handy.
Typically, you’ll see age detection implemented as a two-stage process:
import cv2
import math
import argparse
def highlightFace(net, frame, conf_threshold=0.7):
frameOpencvDnn=frame.copy()
frameHeight=frameOpencvDnn.shape[0]
frameWidth=frameOpencvDnn.shape[1]
blob=cv2.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False)
net.setInput(blob)
detections=net.forward()
faceBoxes=[]
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)
faceBoxes.append([x1,y1,x2,y2])
cv2.rectangle(frameOpencvDnn, (x1,y1), (x2,y2), (0,255,0), int(round(frameHeight/150)), 8)
return frameOpencvDnn,faceBoxes
faceProto="opencv_face_detector.pbtxt"
faceModel="opencv_face_detector_uint8.pb"
ageProto="age_deploy.prototxt"
ageModel="age_net.caffemodel"
genderProto="gender_deploy.prototxt"
genderModel="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']
faceNet=cv2.dnn.readNet(faceModel,faceProto)
ageNet=cv2.dnn.readNet(ageModel,ageProto)
genderNet=cv2.dnn.readNet(genderModel,genderProto)
video=cv2.VideoCapture('karan.jpg')
padding=20
while cv2.waitKey(1)<0:
hasFrame,frame=video.read()
if not hasFrame:
cv2.waitKey()
break
resultImg,faceBoxes=highlightFace(faceNet,frame)
if not faceBoxes:
print("No face detected")
for faceBox in faceBoxes:
face=frame[max(0,faceBox[1]-padding):
min(faceBox[3]+padding,frame.shape[0]-1),max(0,faceBox[0]-padding)
:min(faceBox[2]+padding, frame.shape[1]-1)]
blob=cv2.dnn.blobFromImage(face, 1.0, (227,227), MODEL_MEAN_VALUES, swapRB=False)
genderNet.setInput(blob)
genderPreds=genderNet.forward()
gender=genderList[genderPreds[0].argmax()]
print(f'Gender: {gender}')
ageNet.setInput(blob)
agePreds=ageNet.forward()
age=ageList[agePreds[0].argmax()]
print(f'Age: {age[1:-1]} years')
cv2.putText(resultImg, f'{gender}, {age}', (faceBox[0], faceBox[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,255), 2, cv2.LINE_AA)
cv2.imshow("Detecting age and gender", resultImg)
Gender: Male Age: 25-32 years