基于深度学习神经网络MTCNN的人脸检测系统源码

一. mtcnn概述

MTCNN，英文全称是Multi-task convolutional neural network，中文全称是多任务卷积神经网络，该神经网络将人脸区域检测与人脸关键点检测放在了一起。

二. mtcnn的网络结构

mtcnn从整体上划分分为P-Net、R-Net、和O-Net三层网络结构。各层的作用直观上感受如下图所示：

其网络结构：

三. mtcnn的网络结构代码

import tensorflow as tf class PNet(tf.keras.Model): def __init__(self): super().__init__() self.conv1 = tf.keras.layers.Conv2D(10, 3, 1, name='conv1') self.prelu1 = tf.keras.layers.PReLU(shared_axes=[1,2], name="PReLU1") self.conv2 = tf.keras.layers.Conv2D(16, 3, 1, name='conv2') self.prelu2 = tf.keras.layers.PReLU(shared_axes=[1,2], name="PReLU2") self.conv3 = tf.keras.layers.Conv2D(32, 3, 1, name='conv3') self.prelu3 = tf.keras.layers.PReLU(shared_axes=[1,2], name="PReLU3") self.conv4_1 = tf.keras.layers.Conv2D(2, 1, 1, name='conv4-1') self.conv4_2 = tf.keras.layers.Conv2D(4, 1, 1, name='conv4-2') def call(self, x, training=False): out = self.prelu1(self.conv1(x)) out = tf.nn.max_pool2d(out, 2, 2, padding="SAME") out = self.prelu2(self.conv2(out)) out = self.prelu3(self.conv3(out)) score = tf.nn.softmax(self.conv4_1(out), axis=-1) boxes = self.conv4_2(out) return boxes, score class RNet(tf.keras.Model): def __init__(self): super().__init__() self.conv1 = tf.keras.layers.Conv2D(28, 3, 1, name='conv1') self.prelu1 = tf.keras.layers.PReLU(shared_axes=[1,2], name="prelu1") self.conv2 = tf.keras.layers.Conv2D(48, 3, 1, name='conv2') self.prelu2 = tf.keras.layers.PReLU(shared_axes=[1,2], name="prelu2") self.conv3 = tf.keras.layers.Conv2D(64, 2, 1, name='conv3') self.prelu3 = tf.keras.layers.PReLU(shared_axes=[1,2], name="prelu3") self.dense4 = tf.keras.layers.Dense(128, name='conv4') self.prelu4 = tf.keras.layers.PReLU(shared_axes=None, name="prelu4") self.dense5_1 = tf.keras.layers.Dense(2, name="conv5-1") self.dense5_2 = tf.keras.layers.Dense(4, name="conv5-2") self.flatten = tf.keras.layers.Flatten() def call(self, x, training=False): out = self.prelu1(self.conv1(x)) out = tf.nn.max_pool2d(out, 3, 2, padding="SAME") out = self.prelu2(self.conv2(out)) out = tf.nn.max_pool2d(out, 3, 2, padding="VALID") out = self.prelu3(self.conv3(out)) out = self.flatten(out) out = self.prelu4(self.dense4(out)) score = tf.nn.softmax(self.dense5_1(out), -1) boxes = self.dense5_2(out) return boxes, score class ONet(tf.keras.Model): def __init__(self): super().__init__() self.conv1 = tf.keras.layers.Conv2D(32, 3, 1, name="conv1") self.prelu1 = tf.keras.layers.PReLU(shared_axes=[1,2], name="prelu1") self.conv2 = tf.keras.layers.Conv2D(64, 3, 1, name="conv2") self.prelu2 = tf.keras.layers.PReLU(shared_axes=[1,2], name="prelu2") self.conv3 = tf.keras.layers.Conv2D(64, 3, 1, name="conv3") self.prelu3 = tf.keras.layers.PReLU(shared_axes=[1,2], name="prelu3") self.conv4 = tf.keras.layers.Conv2D(128, 2, 1, name="conv4") self.prelu4 = tf.keras.layers.PReLU(shared_axes=[1,2], name="prelu4") self.dense5 = tf.keras.layers.Dense(256, name="conv5") self.prelu5 = tf.keras.layers.PReLU(shared_axes=None, name="prelu5") self.dense6_1 = tf.keras.layers.Dense(2 , name="conv6-1") self.dense6_2 = tf.keras.layers.Dense(4 , name="conv6-2") self.dense6_3 = tf.keras.layers.Dense(10 , name="conv6-3") self.flatten = tf.keras.layers.Flatten() def call(self, x, training=False): out = self.prelu1(self.conv1(x)) out = tf.nn.max_pool2d(out, 3, 2, padding="SAME") out = self.prelu2(self.conv2(out)) out = tf.nn.max_pool2d(out, 3, 2, padding="VALID") out = self.prelu3(self.conv3(out)) out = tf.nn.max_pool2d(out, 2, 2, padding="SAME") out = self.prelu4(self.conv4(out)) out = self.dense5(self.flatten(out)) out = self.prelu5(out) score = tf.nn.softmax(self.dense6_1(out)) boxes = self.dense6_2(out) lamks = self.dense6_3(out) return boxes, lamks, score

四. mtcnn的演示效果

五. 整个工程的内容

提供源代码，模型，提供GUI界面代码，主要使用方法可以参考里面的“文档说明_必看.docx”

项目完整文件下载请见演示与介绍视频的简介处给出：➷➷➷

https://www.bilibili.com/video/BV1rCU6Y1EbX/