基于深度学习的面部表情识别 (Facial-expression Recognition)
一、项目背景
数据集cnn_train.csv包含人类面部表情的图片的label和feature。在这里,面部表情识别相当于一个分类问题,共有7个类别。
其中label包括7种类型表情:
7-classes
一共有28709个label,说明包含了28709张表情包。
每一行就是一张表情包4848=2304个像素,相当于4848个灰度值(intensity)(0为黑, 255为白)
二、数据预处理
1.标签与特征分离
cnn_feature_label.py
对原数据进行处理,分离后分别保存为cnn_label.csv和cnn_data.csv.()
2.数据可视化
face_view.py
对特征进一步处理,也就是将每个数据行的2304个像素值合成每张48*48的表情图,最后做成24000张表情包。
3.分割训练集和测试集
cnn_picture_label.py
Step1:划分一下训练集和验证集。一共有28709张图片,我取前24000张图片作为训练集,其他图片作为验证集。新建文件夹cnn_train和cnn_val,将0.jpg到23999.jpg放进文件夹cnn_train,将其他图片放进文件夹cnn_val.
Step2:对每张图片标记属于哪一个类别,存放在dataset.csv中,分别在刚刚训练集和测试集执行标记任务。
Step3:重写Dataset类,它是Pytorch中图像数据集加载的一个基类,需要重写类来实现加载上面的图像数据集 (rewrite_dataset.py)
三、搭建模型
CNN_face.py
neural_network
四、训练模型
train.py
损失函数使用交叉熵,优化器是随机梯度下降SGD,其中weight_decay为正则项系数,每轮训练打印损失值,每5轮训练打印准确率。
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温馨提示
Pytorch实现基于深度学习卷积神经网络的面部表情识别项目源码+面部表情数据集(人脸面部表情识别项目).zip该项目是个人毕设项目源码,评审分达到97分,都经过严格调试,确保可以运行!放心下载使用。该项目资源主要针对计算机相关专业的学生或从业者下载使用,也可作为期末课程设计、课程大作业、毕业设计等。 以下是一般步骤: 数据集收集和准备:收集包含不同面部表情的图像数据集,并将其划分为训练集和测试集。确保每个样本都有对应的标签(例如,Happy、Sad、Angry等)。 数据预处理:对图像数据进行预处理,如缩放、裁剪或归一化。这可以帮助提高模型的训练效果。 构建卷积神经网络模型:使用PyTorch构建卷积神经网络模型。通常使用一些卷积层、池化层和全连接层来构建模型。可以选择不同的网络架构,如VGG、ResNet或自定义模型。 定义损失函数和优化器:选择适当的损失函数(如交叉熵损失)用于衡量模型输出与真实标签之间的差异,并选择合适的优化器(如SGD、Adam等)来更新模型参数。 训练模型:使用训练集对模型进行训练。迭代传递数据样本并计算损失,然后通过反向传播更新模型参数。重复此过
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