一：dlib的shape_predictor_68_face_landmarks模型

该模型能够检测人脸的68个特征点（facial landmarks），定位图像中的眼睛，眉毛，鼻子，嘴巴，下颌线（ROI，Region of Interest）

下颌线[1,17]左眼眉毛[18,22]右眼眉毛[23,27]鼻梁[28,31]鼻子[32,36]左眼[37,42]右眼[43,48]       上嘴唇外边缘[49,55]  上嘴唇内边缘[66,68]   下嘴唇外边缘[56,60]  下嘴唇内边缘[61,65]

在使用的过程中对应的下标要减1，像数组的下标是从0开始。

二、眨眼检测

基本原理：计算眼睛长宽比 Eye Aspect Ratio，EAR.当人眼睁开时，EAR在某个值上下波动，当人眼闭合时，EAR迅速下降，理论上会接近于零，当时人脸检测模型还没有这么精确。所以我们认为当EAR低于某个阈值时，眼睛处于闭合状态。为检测眨眼次数，需要设置同一次眨眼的连续帧数。眨眼速度比较快，一般1~3帧就完成了眨眼动作。两个阈值都要根据实际情况设置。

 程序实现：

from imutils import face_utilsimport numpy as npimport dlibimport cv2# 眼长宽比例def eye_aspect_ratio(eye):    # (|e1-e5|+|e2-e4|) / (2|e0-e3|)    A = np.linalg.norm(eye[1] - eye[5])    B = np.linalg.norm(eye[2] - eye[4])    C = np.linalg.norm(eye[0] - eye[3])    ear = (A + B) / (2.0 * C)    return ear#  进行活体检测（包含眨眼和张嘴）def liveness_detection():    vs = cv2.VideoCapture(0)  # 调用第一个摄像头的信息    # 眼长宽比例值    EAR_THRESH = 0.15    EAR_CONSEC_FRAMES_MIN = 1    EAR_CONSEC_FRAMES_MAX = 3  # 当EAR小于阈值时，接连多少帧一定发生眨眼动作    # 初始化眨眼的连续帧数    blink_counter = 0    # 初始化眨眼次数总数    blink_total = 0    print("[INFO] loading facial landmark predictor...")    # 人脸检测器    detector = dlib.get_frontal_face_detector()    # 特征点检测器    predictor = dlib.shape_predictor("model/shape_predictor_68_face_landmarks.dat")    # 获取左眼的特征点    (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]    # 获取右眼的特征点    (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]    print("[INFO] starting video stream thread...")    while True:        flag, frame = vs.read()  # 返回一帧的数据        if not flag:            print("不支持摄像头", flag)            break        if frame is not None:            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # 转成灰度图像            rects = detector(gray, 0)  # 人脸检测            # 只能处理一张人脸            if len(rects) == 1:                shape = predictor(gray, rects[0])  # 保存68个特征点坐标的<class 'dlib.dlib.full_object_detection'>对象                shape = face_utils.shape_to_np(shape)  # 将shape转换为numpy数组，数组中每个元素为特征点坐标                left_eye = shape[lStart:lEnd]  # 取出左眼对应的特征点                right_eye = shape[rStart:rEnd]  # 取出右眼对应的特征点                left_ear = eye_aspect_ratio(left_eye)  # 计算左眼EAR                right_ear = eye_aspect_ratio(right_eye)  # 计算右眼EAR                ear = (left_ear + right_ear) / 2.0   # 求左右眼EAR的均值                left_eye_hull = cv2.convexHull(left_eye)  # 寻找左眼轮廓                right_eye_hull = cv2.convexHull(right_eye)  # 寻找右眼轮廓                # mouth_hull = cv2.convexHull(mouth)  # 寻找嘴巴轮廓                cv2.drawContours(frame, [left_eye_hull], -1, (0, 255, 0), 1)   # 绘制左眼轮廓                cv2.drawContours(frame, [right_eye_hull], -1, (0, 255, 0), 1)  # 绘制右眼轮廓                # EAR低于阈值，有可能发生眨眼，眨眼连续帧数加一次                if ear < EAR_THRESH:                    blink_counter += 1                # EAR高于阈值，判断前面连续闭眼帧数，如果在合理范围内，说明发生眨眼                else:                    if EAR_CONSEC_FRAMES_MIN <= blink_counter <= EAR_CONSEC_FRAMES_MAX:                        blink_total += 1                    blink_counter = 0                cv2.putText(frame, "Blinks: {}".format(blink_total), (0, 30),                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)                cv2.putText(frame, "EAR: {:.2f}".format(ear), (300, 30),                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)            elif len(rects) == 0:                cv2.putText(frame, "No face!", (0, 30),                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)            else:                cv2.putText(frame, "More than one face!", (0, 30),                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)            cv2.namedWindow("Frame", cv2.WINDOW_NORMAL)            cv2.imshow("Frame", frame)            # 按下q键退出循环（鼠标要点击一下图片使图片获得焦点）            if cv2.waitKey(1) & 0xFF == ord('q'):                break    cv2.destroyAllWindows()    vs.release()liveness_detection()

三、张口检测

检测原理：类似眨眼检测，计算Mouth Aspect Ratio,MAR.当MAR大于设定的阈值时，认为张开了嘴巴。

1：采用的判定是张开后闭合计算一次张嘴动作。

mar     # 嘴长宽比例

MAR_THRESH = 0.2    # 嘴长宽比例值

mouth_status_open   # 初始化张嘴状态为闭嘴

当mar大于设定的比例值表示张开，张开后闭合代表一次张嘴动作

   # 通过张、闭来判断一次张嘴动作                if mar > MAR_THRESH:                     mouth_status_open = 1                else:                    if mouth_status_open:                        mouth_total += 1                    mouth_status_open = 0

2： 嘴长宽比例的计算

# 嘴长宽比例def mouth_aspect_ratio(mouth):    A = np.linalg.norm(mouth[1] - mouth[7])  # 61, 67    B = np.linalg.norm(mouth[3] - mouth[5])  # 63, 65    C = np.linalg.norm(mouth[0] - mouth[4])  # 60, 64    mar = (A + B) / (2.0 * C)    return mar

原本采用嘴唇外边缘来计算，发现嘟嘴也会被判定为张嘴，故才用嘴唇内边缘进行计算，会更加准确。

这里mouth下标的值取决于取的是“mouth”还是“inner_mouth”，由于我要画的轮廓是内嘴，所以我采用的是inner_mouth

 (mStart, mEnd) = face_utils.FACIAL_LANDMARKS_IDXS["inner_mouth"]

打开以下方法，进入到源码，可以看到每个特征点对应的下标是不一样的，对应的mouth特征点的下标也是不同的

 （以上的区间包左边代表开始下标，右边值-1）从上面可知mouth是从（48,68），inner_mouth从(60, 68)，mouth包含inner_mouth,如果取得是mouth的值，则嘴长宽比例的计算如下

# 嘴长宽比例def mouth_aspect_ratio(mouth):    # (|m13-m19|+|m15-m17|)/(2|m12-m16|)    A = np.linalg.norm(mouth[13] - mouth[19])  # 61, 67    B = np.linalg.norm(mouth[15] - mouth[17])  # 63, 65    C = np.linalg.norm(mouth[12] - mouth[16])  # 60, 64    mar = (A + B) / (2.0 * C)    return mar

3：完整程序实现如下

from imutils import face_utilsimport numpy as npimport dlibimport cv2# 嘴长宽比例def mouth_aspect_ratio(mouth):    A = np.linalg.norm(mouth[1] - mouth[7])  # 61, 67    B = np.linalg.norm(mouth[3] - mouth[5])  # 63, 65    C = np.linalg.norm(mouth[0] - mouth[4])  # 60, 64    mar = (A + B) / (2.0 * C)    return mar#  进行活体检测（张嘴）def liveness_detection():    vs = cv2.VideoCapture(0)  # 调用第一个摄像头的信息    # 嘴长宽比例值    MAR_THRESH = 0.2    # 初始化张嘴次数    mouth_total = 0    # 初始化张嘴状态为闭嘴    mouth_status_open = 0    print("[INFO] loading facial landmark predictor...")    # 人脸检测器    detector = dlib.get_frontal_face_detector()    # 特征点检测器    predictor = dlib.shape_predictor("model/shape_predictor_68_face_landmarks.dat")    # 获取嘴巴特征点    (mStart, mEnd) = face_utils.FACIAL_LANDMARKS_IDXS["inner_mouth"]    print("[INFO] starting video stream thread...")    while True:        flag, frame = vs.read()  # 返回一帧的数据        if not flag:            print("不支持摄像头", flag)            break        if frame is not None:            # 图片转换成灰色（去除色彩干扰，让图片识别更准确）            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)            rects = detector(gray, 0)  # 人脸检测            # 只能处理一张人脸            if len(rects) == 1:                shape = predictor(gray, rects[0])  # 保存68个特征点坐标的<class 'dlib.dlib.full_object_detection'>对象                shape = face_utils.shape_to_np(shape)  # 将shape转换为numpy数组，数组中每个元素为特征点坐标                inner_mouth = shape[mStart:mEnd]   # 取出嘴巴对应的特征点                mar = mouth_aspect_ratio(inner_mouth)  # 求嘴巴mar的均值                mouth_hull = cv2.convexHull(inner_mouth)  # 寻找内嘴巴轮廓                cv2.drawContours(frame, [mouth_hull], -1, (0, 255, 0), 1)  # 绘制嘴巴轮廓                # 通过张、闭来判断一次张嘴动作                if mar > MAR_THRESH:                     mouth_status_open = 1                else:                    if mouth_status_open:                        mouth_total += 1                    mouth_status_open = 0                cv2.putText(frame, "Mouth: {}".format(mouth_total),                            (130, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)                cv2.putText(frame, "MAR: {:.2f}".format(mar), (450, 30),                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)            elif len(rects) == 0:                cv2.putText(frame, "No face!", (0, 30),                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)            else:                cv2.putText(frame, "More than one face!", (0, 30),                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)            cv2.namedWindow("Frame", cv2.WINDOW_NORMAL)            cv2.imshow("Frame", frame)            # 按下q键退出循环（鼠标要点击一下图片使图片获得焦点）            if cv2.waitKey(1) & 0xFF == ord('q'):                break    cv2.destroyAllWindows()    vs.release()liveness_detection()

三：眨眼和张嘴结合（摄像头）

from imutils import face_utilsimport numpy as npimport dlibimport cv2# 眼长宽比例def eye_aspect_ratio(eye):    # (|e1-e5|+|e2-e4|) / (2|e0-e3|)    A = np.linalg.norm(eye[1] - eye[5])    B = np.linalg.norm(eye[2] - eye[4])    C = np.linalg.norm(eye[0] - eye[3])    ear = (A + B) / (2.0 * C)    return ear# 嘴长宽比例def mouth_aspect_ratio(mouth):    A = np.linalg.norm(mouth[1] - mouth[7])  # 61, 67    B = np.linalg.norm(mouth[3] - mouth[5])  # 63, 65    C = np.linalg.norm(mouth[0] - mouth[4])  # 60, 64    mar = (A + B) / (2.0 * C)    return mar#  进行活体检测（包含眨眼和张嘴）def liveness_detection():    vs = cv2.VideoCapture(0)  # 调用第一个摄像头的信息    # 眼长宽比例值    EAR_THRESH = 0.15    EAR_CONSEC_FRAMES_MIN = 1    EAR_CONSEC_FRAMES_MAX = 5  # 当EAR小于阈值时，接连多少帧一定发生眨眼动作    # 嘴长宽比例值    MAR_THRESH = 0.2    # 初始化眨眼的连续帧数    blink_counter = 0    # 初始化眨眼次数总数    blink_total = 0    # 初始化张嘴次数    mouth_total = 0    # 初始化张嘴状态为闭嘴    mouth_status_open = 0    print("[INFO] loading facial landmark predictor...")    # 人脸检测器    detector = dlib.get_frontal_face_detector()    # 特征点检测器    predictor = dlib.shape_predictor("model/shape_predictor_68_face_landmarks.dat")    # 获取左眼的特征点    (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]    # 获取右眼的特征点    (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]    # 获取嘴巴特征点    (mStart, mEnd) = face_utils.FACIAL_LANDMARKS_IDXS["inner_mouth"]    print("[INFO] starting video stream thread...")    while True:        flag, frame = vs.read()  # 返回一帧的数据        if not flag:            print("不支持摄像头", flag)            break        if frame is not None:            # 图片转换成灰色（去除色彩干扰，让图片识别更准确）            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)            rects = detector(gray, 0)  # 人脸检测            # 只能处理一张人脸            if len(rects) == 1:                shape = predictor(gray, rects[0])  # 保存68个特征点坐标的<class 'dlib.dlib.full_object_detection'>对象                shape = face_utils.shape_to_np(shape)  # 将shape转换为numpy数组，数组中每个元素为特征点坐标                left_eye = shape[lStart:lEnd]  # 取出左眼对应的特征点                right_eye = shape[rStart:rEnd]  # 取出右眼对应的特征点                left_ear = eye_aspect_ratio(left_eye)  # 计算左眼EAR                right_ear = eye_aspect_ratio(right_eye)  # 计算右眼EAR                ear = (left_ear + right_ear) / 2.0   # 求左右眼EAR的均值                inner_mouth = shape[mStart:mEnd]  # 取出嘴巴对应的特征点                mar = mouth_aspect_ratio(inner_mouth)  # 求嘴巴mar的均值                left_eye_hull = cv2.convexHull(left_eye)  # 寻找左眼轮廓                right_eye_hull = cv2.convexHull(right_eye)  # 寻找右眼轮廓                mouth_hull = cv2.convexHull(inner_mouth)  # 寻找内嘴巴轮廓                cv2.drawContours(frame, [left_eye_hull], -1, (0, 255, 0), 1)   # 绘制左眼轮廓                cv2.drawContours(frame, [right_eye_hull], -1, (0, 255, 0), 1)  # 绘制右眼轮廓                cv2.drawContours(frame, [mouth_hull], -1, (0, 255, 0), 1)  # 绘制嘴巴轮廓                # EAR低于阈值，有可能发生眨眼，眨眼连续帧数加一次                if ear < EAR_THRESH:                    blink_counter += 1                # EAR高于阈值，判断前面连续闭眼帧数，如果在合理范围内，说明发生眨眼                else:                    # if the eyes were closed for a sufficient number of                    # then increment the total number of blinks                    if EAR_CONSEC_FRAMES_MIN <= blink_counter <= EAR_CONSEC_FRAMES_MAX:                        blink_total += 1                    blink_counter = 0                # 通过张、闭来判断一次张嘴动作                if mar > MAR_THRESH:                     mouth_status_open = 1                else:                    if mouth_status_open:                        mouth_total += 1                    mouth_status_open = 0                cv2.putText(frame, "Blinks: {}".format(blink_total), (0, 30),                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)                cv2.putText(frame, "Mouth: {}".format(mouth_total),                            (130, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)                cv2.putText(frame, "EAR: {:.2f}".format(ear), (300, 30),                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)                cv2.putText(frame, "MAR: {:.2f}".format(mar), (450, 30),                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)            elif len(rects) == 0:                cv2.putText(frame, "No face!", (0, 30),                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)            else:                cv2.putText(frame, "More than one face!", (0, 30),                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)            cv2.namedWindow("Frame", cv2.WINDOW_NORMAL)            cv2.imshow("Frame", frame)            # 按下q键退出循环（鼠标要点击一下图片使图片获得焦点）            if cv2.waitKey(1) & 0xFF == ord('q'):                break    cv2.destroyAllWindows()    vs.release()#  调用摄像头进行张嘴眨眼活体检测liveness_detection()

四：采用视频进行活体检测

最大的区别是原来通过摄像头获取一帧一帧的视频流进行判断，现在是通过视频获取一帧一帧的视频流进行判断

1：先看下获取摄像头的图像信息 

# -*-coding:GBK -*-import cv2from PIL import Image, ImageDrawimport numpy as np# 1.调用摄像头# 2.读取摄像头图像信息# 3.在图像上添加文字信息# 4.保存图像cap = cv2.VideoCapture(0)  # 调用第一个摄像头信息while True:    flag, frame = cap.read()  # 返回一帧的数据    # #返回值：flag：bool值：True：读取到图片，False：没有读取到图片  frame：一帧的图片    # BGR是cv2 的图像保存格式，RGB是PIL的图像保存格式，在转换时需要做格式上的转换    img_PIL = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))    draw = ImageDraw.Draw(img_PIL)    draw.text((100, 100), 'press q to exit', fill=(255, 255, 255))    # 将frame对象转换成cv2的格式    frame = cv2.cvtColor(np.array(img_PIL), cv2.COLOR_RGB2BGR)    cv2.imshow('capture', frame)    if cv2.waitKey(1) & 0xFF == ord('q'):        cv2.imwrite('images/out.jpg', frame)        breakcap.release()

2：获取视频的图像信息 

# -*-coding:GBK -*-import cv2from PIL import Image, ImageDrawimport numpy as np# 1.调用摄像头# 2.读取摄像头图像信息# 3.在图像上添加文字信息# 4.保存图像cap = cv2.VideoCapture(r'video\face13.mp4')  # 调用第一个摄像头信息while True:    flag, frame = cap.read()  # 返回一帧的数据    if not flag:        break    if frame is not None:        # BGR是cv2 的图像保存格式，RGB是PIL的图像保存格式，在转换时需要做格式上的转换        img_PIL = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))        draw = ImageDraw.Draw(img_PIL)        draw.text((100, 100), 'press q to exit', fill=(255, 255, 255))        # # 将frame对象转换成cv2的格式        frame = cv2.cvtColor(np.array(img_PIL), cv2.COLOR_RGB2BGR)        cv2.imshow('capture', frame)        if cv2.waitKey(1) & 0xFF == ord('q'):            cv2.imwrite('images/out.jpg', frame)            breakcv2.destroyAllWindows()cap.release()

五：视频进行人脸识别和活体检测

1：原理

计算当出现1次眨眼或1次张嘴就判断为活人，记录下一帧的人脸图片，和要判定的人员图片进行比对，获取比对后的相似度，进行判断是否是同一个人，为了增加判断的速度，才用2帧进行一次活体检测判断。

2：代码实现

import face_recognitionfrom imutils import face_utilsimport numpy as npimport dlibimport cv2import sys# 初始化眨眼次数blink_total = 0# 初始化张嘴次数mouth_total = 0# 设置图片存储路径pic_path = r'images\viode_face.jpg'# 图片数量pic_total = 0# 初始化眨眼的连续帧数以及总的眨眼次数blink_counter = 0# 初始化张嘴状态为闭嘴mouth_status_open = 0def getFaceEncoding(src):    image = face_recognition.load_image_file(src)  # 加载人脸图片    # 获取图片人脸定位[(top,right,bottom,left )]    face_locations = face_recognition.face_locations(image)    img_ = image[face_locations[0][0]:face_locations[0][2], face_locations[0][3]:face_locations[0][1]]    img_ = cv2.cvtColor(img_, cv2.COLOR_BGR2RGB)    # display(img_)    face_encoding = face_recognition.face_encodings(image, face_locations)[0]  # 对人脸图片进行编码    return face_encodingdef simcos(a, b):    a = np.array(a)    b = np.array(b)    dist = np.linalg.norm(a - b)  # 二范数    sim = 1.0 / (1.0 + dist)  #    return sim# 提供对外比对的接口 返回比对的相似度def comparison(face_src1, face_src2):    xl1 = getFaceEncoding(face_src1)    xl2 = getFaceEncoding(face_src2)    value = simcos(xl1, xl2)    print(value)# 眼长宽比例def eye_aspect_ratio(eye):    # (|e1-e5|+|e2-e4|) / (2|e0-e3|)    A = np.linalg.norm(eye[1] - eye[5])    B = np.linalg.norm(eye[2] - eye[4])    C = np.linalg.norm(eye[0] - eye[3])    ear = (A + B) / (2.0 * C)    return ear# 嘴长宽比例def mouth_aspect_ratio(mouth):    A = np.linalg.norm(mouth[1] - mouth[7])  # 61, 67    B = np.linalg.norm(mouth[3] - mouth[5])  # 63, 65    C = np.linalg.norm(mouth[0] - mouth[4])  # 60, 64    mar = (A + B) / (2.0 * C)    return mar#  进行活体检测（包含眨眼和张嘴）#  filePath 视频路径def liveness_detection():    global blink_total  # 使用global声明blink_total，在函数中就可以修改全局变量的值    global mouth_total    global pic_total    global blink_counter    global mouth_status_open    # 眼长宽比例值    EAR_THRESH = 0.15    EAR_CONSEC_FRAMES_MIN = 1    EAR_CONSEC_FRAMES_MAX = 5  # 当EAR小于阈值时，接连多少帧一定发生眨眼动作    # 嘴长宽比例值    MAR_THRESH = 0.2    # 人脸检测器    detector = dlib.get_frontal_face_detector()    # 特征点检测器    predictor = dlib.shape_predictor("model/shape_predictor_68_face_landmarks.dat")    # 获取左眼的特征点    (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]    # 获取右眼的特征点    (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]    # 获取嘴巴特征点    (mStart, mEnd) = face_utils.FACIAL_LANDMARKS_IDXS["inner_mouth"]    vs = cv2.VideoCapture(video_path)    # 总帧数(frames)    frames = vs.get(cv2.CAP_PROP_FRAME_COUNT)    frames_total = int(frames)    for i in range(frames_total):        ok, frame = vs.read(i)  # 读取视频流的一帧        if not ok:            break        if frame is not None and i % 2 == 0:            # 图片转换成灰色（去除色彩干扰，让图片识别更准确）            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)            rects = detector(gray, 0)  # 人脸检测            # 只能处理一张人脸            if len(rects) == 1:                if pic_total == 0:                    cv2.imwrite(pic_path, frame)  # 存储为图像,保存名为 文件夹名_数字（第几个文件）.jpg                    cv2.waitKey(1)                    pic_total += 1                shape = predictor(gray, rects[0])  # 保存68个特征点坐标的<class 'dlib.dlib.full_object_detection'>对象                shape = face_utils.shape_to_np(shape)  # 将shape转换为numpy数组，数组中每个元素为特征点坐标                left_eye = shape[lStart:lEnd]  # 取出左眼对应的特征点                right_eye = shape[rStart:rEnd]  # 取出右眼对应的特征点                left_ear = eye_aspect_ratio(left_eye)  # 计算左眼EAR                right_ear = eye_aspect_ratio(right_eye)  # 计算右眼EAR                ear = (left_ear + right_ear) / 2.0   # 求左右眼EAR的均值                mouth = shape[mStart:mEnd]   # 取出嘴巴对应的特征点                mar = mouth_aspect_ratio(mouth)  # 求嘴巴mar的均值                # EAR低于阈值，有可能发生眨眼，眨眼连续帧数加一次                if ear < EAR_THRESH:                    blink_counter += 1                # EAR高于阈值，判断前面连续闭眼帧数，如果在合理范围内，说明发生眨眼                else:                    if EAR_CONSEC_FRAMES_MIN <= blink_counter <= EAR_CONSEC_FRAMES_MAX:                        blink_total += 1                    blink_counter = 0                # 通过张、闭来判断一次张嘴动作                if mar > MAR_THRESH:                    mouth_status_open = 1                else:                    if mouth_status_open:                        mouth_total += 1                    mouth_status_open = 0            elif len(rects) == 0 and i == 90:                print("No face!")                break            elif len(rects) > 1:                print("More than one face!")        # 判断眨眼次数大于2、张嘴次数大于1则为活体,退出循环        if blink_total >= 1 or mouth_total >= 1:            break    cv2.destroyAllWindows()    vs.release()# video_path, src = sys.argv[1], sys.argv[2]video_path = r'video\face13.mp4'      # 输入的video文件夹位置# src = r'C:\Users\666\Desktop\zz5.jpg'liveness_detection()print("眨眼次数》》", blink_total)print("张嘴次数》》", mouth_total)# comparison(pic_path, src)

六：涉及到的代码

代码包含face_recognition库所有功能的用例，和上面涉及到的dilb库进行人脸识别的所有代码

使用dilb、face_recognition库实现，眨眼+张嘴的活体检测、和人脸识别功能。包含摄像头和视频-Python文档类资源-CSDN下载

参考：

使用dlib人脸检测模型进行人脸活体检测：眨眼+张口_Lee_01的博客-CSDN博客

python dlib学习（十一）：眨眼检测_hongbin_xu的博客-CSDN博客_眨眼检测算法       

Python开发系统实战项目：人脸识别门禁监控系统_闭关修炼——暂退的博客-CSDN博客_face_encodings