ARCFACE人脸识别代码

### ARCFACE 人脸识别算法实现代码示例 #### 使用 Python 和 TensorFlow 实现 ArcFace 算法 下面展示了一个简单的基于 TensorFlow 2.x 的 ArcFace 实现，适用于人脸识别任务。此代码片段来源于一个开源项目[^4]。 ```python import tensorflow as tf from tensorflow.keras import layers class ArcFaceLayer(layers.Layer): def __init__(self, n_classes=10, s=30.0, m=0.50, easy_margin=False, **kwargs): super(ArcFaceLayer, self).__init__(**kwargs) self.n_classes = n_classes self.s = s self.m = m self.easy_margin = easy_margin self.cos_m = tf.math.cos(m) self.sin_m = tf.math.sin(m) def build(self, input_shape): super(ArcFaceLayer, self).build(input_shape[0]) embd_dim = input_shape[0][-1] self.W = self.add_weight( name='W', shape=(embd_dim, self.n_classes), initializer='glorot_uniform', trainable=True, dtype=self.dtype ) def call(self, inputs): embeddings, labels = inputs norm_embeddings = tf.nn.l2_normalize(embeddings, axis=1) norm_W = tf.nn.l2_normalize(self.W, axis=0) cos_t = tf.matmul(norm_embeddings, norm_W, name='cos_t') sin_t = tf.sqrt(1. - cos_t * cos_t + 1e-8) cos_mt = (cos_t * self.cos_m) - (sin_t * self.sin_m) if self.easy_margin: cond_v = tf.cast(tf.greater(cos_t, 0.), 'float32') keep_val = 1. - self.m else: cond_v = tf.cast(tf.greater(cos_t, self.cos_m), 'float32') keep_val = cos_t - self.m cos_mt_temp = cond_v * cos_mt + (1-cond_v) * keep_val mask = tf.one_hot(labels, depth=self.n_classes, on_value=1., off_value=0.) inv_mask = tf.subtract(1., mask) logit = self.s * ((mask * cos_mt_temp) + (inv_mask * cos_t)) return logit ``` 上述 `ArcFaceLayer` 类定义了 ArcFace 损失函数的核心逻辑，并将其封装成 Keras 自定义层的形式以便于集成到更复杂的神经网络结构中去。 为了完成整个识别过程，还需要构建基础的卷积神经网络来提取人脸图像特征向量，之后再连接这个自定义损失层来进行分类训练： ```python def create_model(n_classes): base_model = tf.keras.applications.ResNet50(weights=None, include_top=False, pooling='avg', input_tensor=tf.keras.Input(shape=(112, 112, 3))) x = base_model.output model_output = ArcFaceLayer(n_classes=n_classes)([x, tf.keras.layers.Input(shape=[], dtype=tf.int32)]) final_model = tf.keras.models.Model(inputs=[base_model.input], outputs=model_output) return final_model ``` 通过这种方式可以快速搭建起一套支持 ArcFace 训练模式的基础框架。