Stochastic computing (SC) is a re-emerging technique to process probability data encoded in digital bit-streams. Its main advantage is that arithmetic operations can be implemented by extremely small and low-power logic circuits. This makes SC suitable for signal-processing applications involving matrix operations whose VLSI implementation is very costly. Previous SC approaches only address basic matrix operations with relatively low accuracy needs. We explore the use of SC to implement a representative complex matrix operation, namely eigenvector computation. We apply it to a training task for visual face recognition, and show that our SC design has performance comparable to its conventional binary counterpart, while being able to trade computation time for accuracy.